Abstract
The investigation of vascular biometric traits has become increasingly popular during the last years. This book chapter provides a comprehensive discussion of the respective state of the art, covering hand-oriented techniques (finger vein, palm vein, (dorsal) hand vein and wrist vein recognition) as well as eye-oriented techniques (retina and sclera recognition). We discuss commercial sensors and systems, major algorithmic approaches in the recognition toolchain, available datasets, public competitions and open-source software, template protection schemes, presentation attack(s) (detection), sample quality assessment, mobile acquisition and acquisition on the move, and finally eventual disease impact on recognition and template privacy issues.
You have full access to this open access chapter, Download chapter PDF
Similar content being viewed by others
Keywords
- Vascular biometrics
- Finger vein recognition
- Hand vein recognition
- Palm vein recognition
- Retina recognition
- Sclera recognition
- Near-infrared
1 Introduction
As the name suggests, vascular biometrics are based on vascular patterns, formed by the blood vessel structure inside the human body.
Historically, Andreas Vesalius already suggested in 1543 that the vessels in the extremities of the body are highly variable in their location and structure. Some 350 years later, a professor of forensic medicine at Padua University, Arrigo Tamassia, stated that no two vessel patterns seen on the back of the hand seem to be identical in any two individuals [23].
This pattern has to be made visible and captured by a suitable biometric scanner device in order to be able to conduct biometric recognition. Two parts of the human body (typically not covered by clothing in practical recognition situations) are the major source to extract vascular patterns for biometric purposes: The human hand [151, 275] (used in finger vein [59, 120, 234, 247, 250, 300] as well as in hand/palm/wrist vein [1, 226] recognition) and the human eye (used in retina [97, 166] and sclera [44] recognition), respectively.
The imaging principles used, however, are fairly different for those biometric modalities. Vasculature in the human hand is at least covered by skin layers and also by other tissue types eventually (depending on the vasculatures’ position depth wrt. skin surface). Therefore, Visible Light (VIS) imaging does not reveal the vessel structures properly.
1.1 Imaging Hand-Based Vascular Biometric Traits
In principle, high-precision imaging of human vascular structures, including those inside the human hand, is a solved problem. Figure 1.1a displays corresponding vessels using a Magnetic Resonance Angiography (MRA) medical imaging device, while Fig. 1.1b shows the result of applying hyperspectral imaging using a STEMMER IMAGING device using their Perception Studio software to visualise the data captured in the range 900–1700 nm. However, biometric sensors have a limitation in terms of their costs. For practical deployment in real-world authentication solutions, the technologies used to produce the images in Fig. 1.1 are not an option for this reason. The solution is much simpler and thus more cost-effective Near-Infrared (NIR) imaging.
Joe Rice (the author of the Foreword of this Handbook) patented his NIR-imaging-based “Veincheck” system in the early 1980s which is often seen as the birth of hand-based vascular biometrics. After the expiry of that patent, Hitachi, Fujitsu and Techsphere launched security products relying on vein biometrics (all holding various patents in this area now). Joe Rice is still involved in this business, as he is partnering with the Swiss company BiowatchID producing wrist vein-based mobile recognition technology (see Sect. 1.2).
The physiological background of this imaging technique is as follows. The haemoglobin in the bloodstream absorbs NIR light. The haemoglobin is the pigment in the blood which is primarily composed of iron, which carries the oxygen. Haemoglobin is known to absorb NIR light. This is why vessels appear as dark structures under NIR illumination, while the surrounding tissue has a much lower light absorption coefficient in that spectrum and thus appears bright. The blood in veins obviously contains a higher amount of deoxygenated haemoglobin as compared to blood in arteries. Oxygenated and deoxygenated haemoglobin absorb NIR light equally at 800 nm, whereas at 760 nm absorption is primarily from deoxygenated haemoglobin while above 800 nm oxygenated haemoglobin exhibits stronger absorption [68, 161]. Thus, the vascular pattern inside the hand can be rendered visible with the help of an NIR light source in combination with an NIR-sensitive image sensor. Depending on the used wavelength of illumination, either both or only a single type of vessels is captured predominantly.
The absorbing property of deoxygenated haemoglobin is also the reason for terming these hand-based modalities as finger vein and hand/palm/wrist vein recognition, while it is actually never demonstrated that it is really only veins and not arteries that are acquired by the corresponding sensors. Finger vein recognition deals with the vascular pattern inside the human fingers (this is the most recent trait in this class, and often [126] is assumed to be its origin), while hand/palm/wrist vein recognition visualises and acquires the pattern of the vessels of the central area (or wrist area) of the hand. Figure 1.2 displays example sample data from public datasets for palm vein, wrist vein and finger vein.
The positioning of the light source relative to the camera and the subject’s finger or hand plays an important role. Here, we distinguish between reflected light and transillumination imaging. Reflected light means that the light source and the camera are placed on the same side of the hand and the light emitted by the source is reflected back to the camera. In transillumination, the light source and the camera are on the opposite side of the hand, i.e. the light penetrates skin and tissue of the hand before it is captured by the camera. Figure 1.3 compares these two imaging principles for the backside of the hand. A further distinction is made (mostly in reflected light imaging) whether the palmar or ventral (i.e. inner) side of the hand (or finger) is acquired, or if the dorsal side is subject to image acquisition. Still, also in transillumination imaging, it is possible to discriminate between palmar and dorsal acquisition (where in palmar acquisition, the camera is placed so to acquire the palmar side of the hand while the light is positioned at the dorsal side). Acquisition for wrist vein recognition is limited to reflected light illumination of the palmar side of the wrist.
1.2 Imaging Eye-Based Vascular Biometric Traits
For the eye-based modalities, VIS imaging is applied to capture vessel structures. The retina is the innermost, light-sensitive layer or “coat”, of shell tissue of the eye. The optic disc or optic nerve head is the point of exit for ganglion cell axons leaving the eye. Because there are no rods or cones covering the optic disc, it corresponds to a small blind spot in each eye. The ophthalmic artery bifurcates and supplies the retina via two distinct vascular networks: The choroidal network, which supplies the choroid and the outer retina, and the retinal network, which supplies the retina’s inner layer. The bifurcations and other physical characteristics of the inner retinal vascular network are known to vary among individuals, which is exploited in retina recognition. Imaging this vascular network is accomplished by fundus photography , i.e. capturing a photograph of the back of the eye, the fundus (which is the interior surface of the eye opposite the lens and includes the retina, optic disc, macula, fovea and posterior pole). Specialised fundus cameras as developed for usage in ophthalmology (thus being a medical device) consist of an intricate microscope (up to 5\(\times \) magnification) attached to a flash-enabled camera, where the annulus-shaped illumination passes through the camera objective lens and through the cornea onto the retina. The light reflected from the retina passes through the un-illuminated hole in the doughnut-shaped illumination system. Illumination is done with white light and acquisition is done either in full colour or employing a green-pass filter (\({\approx }\)540–570 nm) to block out red wavelengths resulting in higher contrast. In medicine, fundus photography is used to monitor, e.g. macular degeneration, retinal neoplasms, choroid disturbances and diabetic retinopathy.
Finally, for sclera recognition, high-resolution VIS eye imagery is required in order to properly depict the fine vessel network being present. Optimal visibility of the vessel network is obtained from two off-angle images in which the eyes look into two directions. Figure 1.4 displays example sample data from public datasets for retina and sclera biometric traits.
1.3 Pros and Cons of Vascular Biometric Traits
Vascular biometrics exhibit certain advantages as compared to other biometric modalities as we shall discuss in the following. However, these modalities have seen commercial deployments to a relatively small extent so far, especially when compared to fingerprint or face recognition-based systems. This might be attributed to some disadvantages also being present for these modalities, which will be also considered subsequently. Of course, not all advantages or disadvantages are shared among all types of vascular biometric modalities, so certain aspects need to be discussed separately and we again discriminate between hand- and eye-based traits.
-
Advantages of hand-based vascular biometrics (finger, hand, and wrist vein recognition): Comparisons are mostly done against fingerprint and palmprint recognition (and against techniques relying on hand geometry to some extent).
-
Vascular biometrics are expected to be insensitive to skin surface conditions (dryness, dirt, lotions) and abrasion (cuts, scars). While the imaging principle strongly suggests this property, so far no empirical evidence has been given to support this.
-
Vascular biometrics enable contactless sensing as there is no necessity to touch the acquiring camera. However, in finger vein recognition, all commercial systems and almost all other sensors being built require the user to place the finger directly on some sensor plate. This is done to ensure position normalisation to some extent and to avoid the camera being dazzled in case of a mal-placed finger (in the transillumination case, the light source could directly illuminate the sensing camera).
-
Vascular biometrics are more resistant against forgeries (i.e. spoofing, presentation attacks) as the vessels are only visible in infrared light. So on the one hand, it is virtually impossible to capture these biometric traits without user consent and from a distance and, on the other hand, it is more difficult to fabricate artefacts to be used in presentation attacks (as these need to be visible in NIR).
-
Liveness detection is easily possible due to detectable blood flow. However, this requires NIR video acquisition and subsequent video analysis and not much work has been done to actually demonstrate this benefit.
-
-
Disadvantages
-
In transillumination imaging (as typically applied for finger veins), the capturing devices need to be built rather large.
-
Images exhibit low contrast and low quality overall caused by the scattering of NIR rays in human tissue. The sharpness of the vessel layout is much lower compared to vessels acquired by retina or sclera imaging. Medical imaging principles like Magnetic Resonance Angiography (MRA) produce high-quality imagery depicting vessels inside the human body; however, these imaging techniques have prohibitive cost for biometric applications.
-
The vascular structure may be influenced by temperature, physical activity, as well as by ageing and injuries/diseases; however, there is almost no empirical evidence that this applies to vessels inside the human hand (see for effects caused by meteorological variance [317]). This book contains a chapter investigating the influence of varying acquisition conditions on finger vein recognition to lay first foundations towards understanding these effects [122].
-
Current commercial sensors do not allow to access, output and store imagery for further investigations and processing. Thus, all available evaluations of these systems have to rely on a black-box principle and only commercial recognition software of the same manufacturer can be used. This situation has motivated the construction of many prototypical devices for research purposes.
-
These modalities cannot be acquired from a distance (which is also an advantage in terms of privacy protection), and it is fairly difficult to acquire them on the move. While at least the first property is beneficial for privacy protection, the combination of both properties excludes hand-based vascular biometrics from free-flow, on-the-move-type application scenarios. However, at least for on-the-move acquisition, advances can be expected in the future [164].
-
-
Advantages of eye-based vascular biometrics (sclera and retina recognition): Comparisons are mostly done against iris, periocular and face recognition.
-
As compared to iris recognition, there is no need to use NIR illumination and imaging. For both modalities, VIS imaging is used.
-
As compared to periocular and face recognition, retina and sclera vascular patterns are much less influenced by intended (e.g. make-up, occlusion like scarfs, etc.) and unintended (e.g. ageing) alterations of the facial area.
-
It is almost impossible to conduct presentation attacks against these modalities—entire eyes cannot be replaced as suggested by the entertainment industry (e.g. “Minority Report”). Full facial masks cannot be used for realistic sclera spoofing.
-
Liveness detection should be easily possible due to detectable blood flow (e.g. video analysis of retina imagery) and pulse detection in sclera video.
-
Not to be counted as an isolated advantage, but sclera-related features can be extracted and fused with other facial related modalities given the visual data is of sufficiently high quality.
-
-
Disadvantages
-
Retina vessel capturing requires to illuminate the background of the eye which is not well received by users. Data acquisition feels like ophthalmological treatment.
-
Vessel structure/vessel width in both retina [171] and sclera [56] is influenced by certain diseases or pathological conditions.
-
Retina capturing devices originate from ophthalmology and thus have a rather high cost (as it is common for medical devices).
-
Currently, there are no commercial solutions available that could prove the practicality of these two biometric modalities.
-
For both modalities, data capture is not possible from a distance (as noted before, this can also be seen as an advantage in terms of privacy protection). For retina recognition, data acquisition is also definitely not possible on-the-move (while this could be an option for sclera given top-end imaging systems in place).
-
In the subsequent sections, we will discuss the following topics for each modality:
-
Commercial sensors and systems;
-
Major algorithmic approaches for preprocessing, feature extraction, template comparison and fusion (published in high-quality scientific outlets);
-
Used datasets (publicly available), competitions and open-source software;
-
Template protection schemes;
-
Presentation attacks, presentation attack detection techniques and sample quality;
-
Mobile acquisition and acquisition on the move.
2 Commercial Sensors and Systems
2.1 Hand-Based Vascular Traits
The area of commercial systems for hand-based vein biometrics is dominated by the two Japanese companies Hitachi and Fujitsu which hold patents for many technical details of the corresponding commercial solutions. This book contains two chapters authored by leading personnel of these two companies [88, 237]. Only in the last few years, competitors have entered the market. Figure 1.5 displays the three currently available finger vein sensors. As clearly visible, the Hitachi sensor is based on a pure transillumination principle, while the other two sensors illuminate the finger from the side while capturing is conducted from below (all sensors capture the palmar side of the finger). Yannan Tech has close connections to a startup from Peking University.
With respect to commercial hand vein systems, the market is even more restricted. Figure 1.6 shows three variants of the Fujitsu PalmSecure system: The “pure” sensor (a), the sensor equipped with a supporting frame to stabilise the hand and restrict the possible positions relative to the sensor (b) and the sensor integrated into a larger device for access control (integration done by a Fujitsu partner company) (c). When comparing the two types of systems, it gets clear that the PalmSecure system can be configured to operate in touchless/contactless manner (where the support frame is suspected to improve in particular genuine comparison scores), while finger vein scanners all require the finger to be placed on the surface of the scanner. While this would not be required in principle, this approach limits the extent of finger rotation and guarantees a rather correct placement of the finger relative to the sensors’ acquisition device. So while it is understandable to choose this design principle, the potential benefit of contactless operation, especially in comparison to fingerprint scanners, is lost.
Techsphere,Footnote 1 being in the business almost right from the start of vascular biometrics, produces dorsal hand vein readers. BiowatchID,Footnote 2 a recent startup, produces a bracelet that is able to read out the wrist pattern and supports various types of authentication solutions. Contrasting to a stationary sensor, this approach represents a per-se mobile solution permanently attached to the person subject to authentication.
Although hand vein-based sensors have been readily available for years, deployments are not seen as frequently as compared to the leading biometric modalities, i.e. face and fingerprint recognition. The most widespread application field of finger vein recognition technology can be observed in finance industry (some examples are illustrated in Fig. 1.7). On the one hand, several financial institutions offer their clients finger vein sensors for secure authentication in home banking. On the other hand, major finger vein equipped ATM roll-outs have been conducted in several countries, e.g. Japan, Poland, Turkey and Hong Kong. The PalmSecure system is mainly used for authentication on Fujitsu-built devices like laptops and tablets and in access control systems.
2.2 Eye-Based Vascular Traits
For vascular biometrics based on retina, commercialisation has not yet reached a mature state (in contrast, first commercial systems have disappeared from the market). Starting very early, the first retina scanners were launched in 1985 by the company EyeDentify and subsequently the company almost established a monopoly in this area. The most recent scanner is the model ICAM 2001, and it seems that this apparatus can still be acquired.Footnote 3 In the first decade of this century, the company Retica Systems Inc. even provided some insight into their template structure called retina code (“Multi-Radius Digital Pattern”,Footnote 4 website no longer active), which has been analysed in earlier work [67]. The proposed template seemed to indicate a low potential for high variability (since the generation is not explained in detail, a reliable statement on this issue is not possible of course). Recall that Retica Systems Inc. claimed a template size of 20–100 bytes, whereas the smallest template investigated in [67] had 225 bytes and did not exhibit sufficient inter-class variability. Deployment of retina recognition technology has been seen mostly in US governmental agencies like CIA, FBI, NASA,Footnote 5 which is a difficult business model for sustainable company development (which might represent a major reason for the low penetration of this technology).
For sclera biometrics, the startup EyeVerify (founded 2012) termed their sclera recognition technology “Eyeprint ID” for which the company also acquired the corresponding patent. After the inclusion of the technology into several mobile banking applications, the company was acquired by Ant Financial, the financial services arm of Alibaba Group in 2016 (their website http://eyeverify.com/ is no longer active).
3 Algorithms in the Recognition Toolchain
Typically, the recognition toolchain consists of several distinct stages, most of which are identical across most vascular traits:
-
1.
Acquisition: Commercial sensors are described in Sect. 1.2, while references to custom developments are given in the tables describing publicly available datasets in Sect. 1.4. The two chapters in this handbook describing sensor technologies provide further details on this topic [113, 258].
-
2.
Image quality assessment: Techniques for this important topic (as required to assess sample quality to demand another acquisition process in case of poor quality or to conduct quality-weighted fusion) are described in Sect. 1.6 for all considered vascular modalities separately.
-
3.
Preprocessing: Typically describes low-level image processing techniques (including normalisation and a variety of enhancement techniques) to cope with varying acquisition conditions, poor contrast, noise and blur. These operations depend on the target modality and are typically even sensor specific. They might also be conducted after the stage mentioned subsequently, but do often assist in RoI determination so that in most cases, the order as suggested here is the typical one.
-
4.
Region of Interest (RoI) determination: This operation describes the process to determine the area in the sample image which is further subjected to feature extraction. In finger vein recognition, the actual finger texture has to be determined, while in palm vein recognition in most cases a rectangular central area of the palm is extracted. For hand and wrist vein recognition, respectively, RoI extraction is hardly consistently done across different methods; still, the RoI is concentrated to contain visual data corresponding to hand tissue only. For retina recognition, the RoI is typically defined by the imaging device and is often a circle of normalised radius around the blind spot. In sclera recognition, this process is of highest importance and is called sclera segmentation, as it segments the sclera area from iris and eyelids.
-
5.
Feature extraction: The ultimate aim of feature extraction is to produce a compact biometric identifier, i.e. the biometric template. As all imagery involving vascular biometrics contain visualised vascular structure, there are basically two options for feature extraction: First, feature extraction directly employs extracted vessel structures, relying on binary images representing these structures, skeletonised versions thereof, graph representations of the generated skeletons or using vein minutiae in the sense of vessel bifurcations or vessel endings. The second option relies on interpreting the RoI as texture patch which is used to extract discriminating features, in many cases key point-related techniques are employed. Deep-learning-based techniques are categorised into this second type of techniques except for those which explicitly extract vascular structure in a segmentation approach. A clear tendency may be observed: The better the quality of the samples and thus the clarity of the vessel structure, the more likely it is to see vein minutiae being used as features. In fundus images with their clear structure, vessels can be identified with high reliability, thus, vessel minutiae are used in most proposals (as fingerprint minutiae-based comparison techniques can be used). On the other hand, sclera vessels are very fine-grained and detailed structures which are difficult to explicitly extract from imagery. Therefore, in many cases, sclera features are more related to texture properties rather than to explicit vascular structure. Hand-based vascular biometrics are somewhat in between, so we see both strategies being applied.
-
6.
Biometric comparison: Two different variants are often seen in literature: The first (and often more efficient) computes distance among extracted templates and compares the found distance to the decision threshold for identifying the correct user, and the second approach applies a classifier to assign a template to the correct class (i.e. the correct user) as stored in the biometric database. This book contains a chapter on efficient template indexing and template comparison in large-scale vein-based identification systems [178].
In most papers on biometric recognition, stages (3)–(5) of this toolchain are presented, discussed, and evaluated. Often, those papers rely on some public (or private) datasets and do not discuss sensor issues. Also, quality assessment is often left out or discussed in separate papers (see Sect. 1.6). A minority of papers discusses certain stages in isolated manner, as also evaluation is more difficult in this setting (e.g. manuscripts on sensor construction, as also contained in this handbook [113, 258], sample quality (see Sect. 1.6), or RoI determination (e.g. on sclera segmentation [217])). In the following, we separately discuss the recognition toolchain of the considered vascular biometric traits and provide many pointers into literature.
A discussion and comparison of the overall recognition performance of vascular biometric traits turns out to be difficult. First, no major commercial players take part in open competitions in this field (contrasting to e.g. fingerprint or face recognition), so the relation between documented recognition accuracy as achieved in these competitions and claimed performance of commercial solutions is not clear. Second, many scientific papers in the field still conduct experiments on private datasets and/or do not release the underlying software for independent verification of the results. As a consequence, many different results are reported and depending on the used dataset and the employed algorithm, reported results sometimes differ by several orders of magnitude (among many examples, see e.g. [114, 258]). Thus, there is urgent need for reproducible research in this field to enable a sensible assessment of vascular traits and a comparison to other biometric modalities.
3.1 Finger Vein Recognition Toolchain
An excellent recent survey covering a significant number of manuscripts in the area of finger vein recognition is [234]. Two other resources provide an overview of hand-based vascular biometrics [151, 275] (where the latter is a monograph) including also finger vein recognition, and also less recent or less comprehensive surveys of finger vein recognition do exist [59, 120, 247, 250, 300] (which still contain a useful collection and description of work in the area).
A review of finger vein preprocessing techniques is provided in [114]. A selection of manuscripts dedicated to this topic is discussed as follows. Yang and Shi [288] analyse the intrinsic factors causing the degradation of finger vein images and propose a simple but effective scattering removal method to improve visibility of the vessel structure. In order to handle the enhancement problem in areas with vasculature effectively, a directional filtering method based on a family of Gabor filters is proposed. The use of Gabor filter in vessel boundary enhancement is almost omnipresent: Multichannel Gabor filters are used to prominently protrude vein vessel information with variances in widths and orientations in images [298]. The vein information in different scales and orientations of Gabor filters is then combined together to generate an enhanced finger vein image. Grey-Level Grouping (GLG) and Circular Gabor Filters (CGF) are proposed for image enhancement [314] by using GLG to reduce illumination fluctuation and improve the contrast of finger vein images, while the CGF strengthens vein ridges in the images. Haze removal techniques based on the Koschmieder’s law can approximatively solve the biological scattering problem as observed in finger vein imagery [236]. Another, yet related approach, is based on a Biological Optical Model (BOM [297]) specific to finger vein imaging according to the principle of light propagation in biological tissues. Based on BOM, the light scattering component is properly estimated and removed for finger vein image restoration.
Techniques for RoI determination are typically described in the context of descriptions of the entire recognition toolchain. There are hardly papers dedicated to this issue separately. A typical example is [287], where an inter-phalangeal joint prior is used for finger vein RoI localisation and haze removal methods with the subsequent application of Gabor filters are used for improving visibility of the vascular structure. The determination of the finger boundaries using a simple \(20\,\times \,4\) mask is proposed in [139], containing two rows of one followed by two rows of \(-1\) for the upper boundary and a horizontally mirrored one for the lower boundary. This approach is further refined in [94], where the finger edges are used to fit a straight line between the detected edges. The parameters of this line are then used to perform an affine transformation which aligns the finger to the centre of the image. A slightly different method is to compute the orientation of the binarised finger RoI using second-order moments and to compensate for the orientation in rotational alignment [130].
The vast majority of papers in the area of finger vein recognition covers the toolchain stages (3)–(5). The systematisation used in the following groups the proposed schemes according to the employed type of features. We start by first discussing feature extraction schemes focusing at the vascular structures in the finger vein imagery, see Table 1.1 for a summarising overview of the existing approaches.
Classical techniques resulting in a binary layout of the vascular network (which is typically used as template and is subject to correlation-based template comparison employing alignment compensation) include repeated line tracking [174], maximum curvature [175], principle curvature [32], mean curvature [244] and wide line detection [94] (where the latter technique proposes a finger rotation compensating template comparison stage). A collection of these features (including the use of spectral minutiae) has also been applied to the dorsal finger side [219] and has been found to be superior to global features such as ordinal codes. Binary finger vein patterns generated using these techniques have been extracted from both the dorsal and palmar finger sides in a comparison [112].
The simplest possible binarisation strategy is adaptive local binarisation, which has been proposed together with a Fourier-domain computation of matching pixels from the resulting vessel structure [248]. Matched filters as well as Gabor filters with subsequent binarisation and morphological post-processing have also been suggested to generate binary vessel structure templates [130]. A repetitive scanning of the images in steps of 15 degrees for strong edges after applying a Sobel edge detector is proposed in combination with superposition of the strong edge responses and subsequent thinning [326]. A fusion of the results when applying this process to several samples leads to the final template.
The more recent techniques focusing on the entire vascular structure take care of potential deformations and misalignment of the vascular network. A matched filtering at various scales is applied to the sample [76], and subsequently local and global characteristics of enhanced vein images are fused to obtain an accurate vein pattern. The extracted structure is then subjected to a geometric deformation compensating template comparison process. Also, [163] introduces a template comparison process, in which a finger-shaped model and non-rigid registration method are used to correct a deformation caused by the finger-posture change. Vessel feature points are extracted based on curvature of image intensity profiles. Another approach considers two levels of vascular structures which are extracted from the orientation map-guided curvature based on the valley- or half valley-shaped cross-sectional profile [299]. After thinning, the reliable vessel branches are defined as vein backbone, which is used to align two images to overcome finger displacement effects. Actual comparison uses elastic matching between the two entire vessel patterns and the degree of overlap between the corresponding vein backbones. A local approach computing vascular pattern in corresponding localised patches instead of the entire images is proposed in [209], template comparison is done in local patches and results are fused. The corresponding patches are identified using mated SIFT key points. Longitudinal rotation correction in both directions using a predefined angle combined with score-level fusion is proposed and successfully applied in [203].
A different approach not explicitly leading to a binary vascular network as template is the employment of a set of Spatial Curve Filters (SCFs) with variations in curvature and orientation [292]. Thus the vascular network consists of vessel curve segments. As finger vessels vary in diameters naturally, a Curve Length Field (CLF) estimation method is proposed to make weighted SCFs adaptive to vein width variations. Finally, with CLF constraints, a vein vector field is built and used to represent the vascular structure used in template comparison.
Subsequent work uses vein minutiae (vessel bifurcations and endings) to represent the vascular structure. In [293], it is proposed to extract each bifurcation point and its local vein branches, named tri-branch vein structure, from the vascular pattern. As these features are particularly well suited to identify imposter mismatches, these are used as first stage in a serial fusion before conducting a second comparison stage using the entire vascular structure. Minutiae pairs are the basis of another feature extraction approach [148], which consists of minutiae pairing based on an SVD-based decomposition of the correlation-weighted proximity matrix. False pairs are removed based on an LBP variant applied locally, and template comparison is conducted based on average similarity degree of the remaining pairs. A fixed-length minutiae-based template representation originating in fingerprint recognition, i.e. minutiae cylinder codes, have also been applied successfully to finger vein imagery [84].
Finally, semantic segmentation convolutional neural networks have been used to extract binary vascular structures subsequently used in classical binary template comparison. The first documented approach uses a combination of vein pixel classifier and a shallow segmentation network [91], while subsequent approaches rely on fully fledged deep segmentation networks and deal with the issue of training data generation regarding the impact of training data quality [100] and a joint training with manually labelled and automatically generated training data [101]. This book contains a chapter extending the latter two approaches [102].
Secondly, we discuss feature extraction schemes interpreting the finger vein sample images as texture image without specific vascular properties. See Table 1.2 for a summarising overview of the existing approaches.
An approach with main emphasis on alignment conducts a fuzzy contrast enhancement algorithm as first stage with subsequent mutual information and affine transformation-based registration technique [11]. Template comparison is conducted by simple correlation assessment. LBP is among the most prominent texture-oriented feature extraction schemes, also for finger vein data. Classical LBP is applied before a fusion of the results of different fingers [290] and the determination of personalised best bits from multiple enrollment samples [289]. Another approach based on classical LBP features applies a vasculature-minutiae-based alignment as first stage [139]. In [138], a Gaussian HP filter is applied before a binarisation with LBP and LDP. Further texture-oriented feature extraction techniques include correlating Fourier phase information of two samples while omitting the high-frequency parts [157] and the development of personalised feature subsets (employing a sparse weight vector) of Pyramid Histograms of Grey, Texture and Orientation Gradients (PHGTOG) [279]. SIFT/SURF keypoints are used for direct template comparison in finger vein samples [114]. A more advanced technique, introducing a deformable finger vein recognition framework [31], extracts PCA-SIFT features and applies bidirectional deformable spatial pyramid comparison.
One of the latest developments is the development usage of learned binary codes of learned binary codes. The first variant [78] is based on multidirectional pixel difference vectors (which are basically simple co-occurrence matrices) which are mapped into low-dimensional binary codes by minimising the information loss between original codes and learned vectors and by conducting a Fisher discriminant analysis (the between-class variation of the local binary features is maximised and the within-class variation of the local binary features is minimised). Each finger vein image is represented as a histogram feature by clustering and pooling these binary codes. A second variant [280] is based on a subject relation graph which captures correlations among subjects. Based on this graph, binary templates are transformed in an optimisation process, in which the distance between templates from different subjects is maximised and templates provide maximal information about subjects.
The topic of learned codes naturally leads to the consideration of deep learning techniques in finger vein recognition. The simplest approach is to extract features from certain layers of pretrained classification networks and to feed those features into a classifier to determine vein pattern similarity to result in a recognition scheme [40, 144]. A corresponding dual-network approach based on combining a Deep Belief Network (FBF-DBN) and a Convolutional Neural Network (CNN) and using vessel feature point image data as input is introduced in [30].
Another approach to apply traditional classification networks is to train the network with the available enrollment data of certain classes (i.e. subjects). A model of a reduced complexity, four-layered CNN classifier with fused convolutional-subsampling architecture for finger vein recognition is proposed for this [228], besides a CNN classifier of similar structure [98]. More advanced is a lightweight two-channel network [60] that has only three convolution layers for finger vein verification. A mini-RoI is extracted from the original images to better solve the displacement problem and used in a second channel of the network. Finally, a two-stream network is presented to integrate the original image and the mini-RoI. This approach, however, has significant drawbacks in case new users have to be enrolled as the networks have to be re-trained, which is not practical.
A more sensible approach is to employ fine-tuned pretrained models of VGG-16, VGG-19, and VGG-face classifiers to determine whether a pair of input images belongs to the same subject or not [89]. Thus, authors eliminated the need for training in case of new enrollment. Similarly, a recent approach [284] uses several known CNN models (namely, light CNN (LCNN), LCNN with triplet similarity loss function, and a modified version of VGG-16) to learn useful feature representations and compare the similarity between finger vein images.
Finally, we aim to discuss certain specific topics in the area of finger vein recognition. It has been suggested to incorporate user individuality, i.e. user role and user gullibility, into the traditional cost-sensitive learning model to further lower misrecognition cost in a finger vein recognition scheme [301]. A study on the individuality of finger vein templates [304] analysing large-scale datasets and corresponding imposter scores showed that at least the considered finger vein templates are sufficiently unique to distinguish one person from another in such large scale datasets. This book contains a chapter [128] on assessing the amount of discriminatory information in finger vein templates. Fusion has been considered in multiple contexts. Different feature extractions schemes have been combined in score-level fusion [114] as well as feature-level fusion [110], while the recognition scores of several fingers have also been combined [290] ([318] aims to identify the finger suited best for finger vein recognition). Multimodal fusion has been enabled by the development of dedicated sensors for this application context, see e.g. for combined fingerprint and finger vein recognition [140, 222]. A fusion of finger vein and finger image features is suggested in [130, 302], where the former technique uses the vascular finger vein structure and normalised texture which are fused into a feature image from which block-based texture is extracted, while the latter fuses the vascular structure binary features at score level with texture features extracted by Radon transform and Gabor filters. Finger vein feature comparison scores (using phase-only correlation) and finger geometry scores (using centroid contour distance) are fused in [10].
A topic of current intensive research is template comparison techniques (and suited feature representations) enabling the compensation of finger rotation and finger deformation [76, 94, 163, 203, 204, 299]. Somewhat related is the consideration multi-perspective finger vein recognition, where two [153] and multiple [205] perspectives are fused to improve recognition results of single-perspective schemes. A chapter in this handbook contains the proposal of a dedicated three-view finger vein scanner [258], while an in-depth analysis of multi-perspective fusion techniques is provided in another one [206].
3.2 Palm Vein Recognition Toolchain
Palm vein recognition techniques are reviewed in [1, 226], while [151, 275] review work on various types of hand-based vein recognition techniques including palm veins. The palm vein recognition toolchain has different requirements compared to the finger vein one, which is also expressed by different techniques being applied. In particular, finger vein sensors typically require the finger to be placed directly on the sensor (not contactless), while palm vein sensors (at least the more recent models) often facilitate a real contactless acquisition. As a consequence, the variability with respect to relative position between hand and sensor can be high, and especially the relative position of sensor plane and hand plane in 3D space may vary significantly causing at least affine changes in the textural representation of the palm vein RoI imagery. Also, RoI extraction is less straightforward compared to finger veins; however, in many cases we see techniques borrowed from palmprint recognition (i.e. extracting a central rectangular area defined by a line found by connecting inter-phalangeal joints). However, it has to be pointed out that most public palm vein datasets do not exhibit these positional variations so that recognition results of many techniques are quite well, but many of these cannot be transferred to real contactless acquisition. We shall notice that the amount of work attempting to rely on the vascular structure directly is much lower, while we see more papers applying local descriptors compared to the finger vein field, see Table 1.3 for an overview of the proposed techniques.
We start by describing approaches targeting the vascular structure. Based on an area maximisation strategy for the RoI, [154] propose a novel parameter selection scheme for Gabor filters used in extracting the vascular network. A directional filter bank involving different orientations is designed to extract the vein pattern [277]; subsequently, the Minimum Directional Code (MDC) is employed to encode the line-based vein features. The imbalance among vessel and non-vessel pixels is considered by evaluating the Directional Filtering Magnitude (DFM) and considered in the code construction to obtain better balance of the binary values. A similar idea based on 2-D Gabor filtering [141] proposes a robust directional coding technique entitled “VeinCode” allowing for compact template representation and fast comparison. The “Junction Points” (JP) set [263], which is formed by the line segments extracted from the sample data, contains position and orientation information of detected line segments and is used as feature. Finally, [9] rely on their approach of applying the Biometric Graph Matching (BGM) to graphs derived from skeletons of the vascular network. See a chapter in this book for a recent overview of this type of methodology [8].
Another group of papers applies local descriptors, obviously with the intention to achieve robustness against positional variations as described before. SIFT features are extracted from registered multiple samples after hierarchical image enhancement and feature-level fusion is applied to result in the final template [286]. Also, [133] applies SIFT to binarised patterns after enhancement, while [193] employs SIFT, SURF and Affine-SIFT as feature extraction to histogram equalised sample data. An approach related to histogram of gradients (HOG) is applied in [72, 187], where after the application of matched filters localised histograms encoding vessel directions (denoted as “histogram of vectors”) are generated as features. It is important to note that this work is based on a custom sensor device which is able to apply reflected light as well as transillumination imaging [72]. Another reflected light palm vein sensor prototype is presented in [238]. After a scaling normalisation of the RoI, [172, 173] apply LBP and LDP for local feature encoding. An improved mutual foreground LBP method is presented [108] in which the LBP extraction process is restricted to neighbourhoods of vessels only by first extracting the vascular network using the principle curvature approach. Multiscale vessel enhancement is targeted in [320, 325] which is implemented by a Hessian-phase-based approach in which the eigenvalues of the second-order derivative of the normalised palm vein images are analysed and used as features. In addition, a localised Radon transform is used as feature extraction and (successfully) compared to the “Laplacianpalm” approach (which finds an embedding that preserves local information by basically computing a local variant of PCA [266]).
Finally, a wavelet scattering approach is suggested [57] with subsequent Spectral Regression Kernel Discriminant Analysis (SRKDA) for dimensionality reduction of the generated templates. A ResNet CNN [309] is proposed for feature extraction on a custom dataset of palm vein imagery with preceding classical RoI detection.
Several authors propose to apply multimodal recognition combining palmprint and palm vein biometrics. In [79], a multispectral fusion of multiscale coefficients of image pairs acquired in different bands (e.g. VIS and NIR) is proposed. The reconstructed images are evaluated in terms of quality but unfortunately no recognition experimentation is conducted. A feature-level fusion of their techniques applied to palm vein and palmprint data is proposed in [187, 263, 266]. The mentioned ResNet approach [309] is also applied to both modalities with subsequent feature fusion.
3.3 (Dorsal) Hand Vein Recognition Toolchain
There are no specific review articles on (dorsal) hand vein recognition, but [151, 275] review work on various types of hand-based vein recognition techniques. Contrasting to the traits discussed so far, there is no commercial sensor available dedicated to acquire dorsal hand vein imagery. Besides the devices used to capture the publicly available datasets, several sensor prototypes have been constructed. For example, [35] use a hyperspectral imaging system to identify the spectral bands suited best to represent the vessel structure. Based on PCA applied to different spectral bands, authors were able to identify two bands which optimise the detection of the dorsal veins. Transillumination is compared to reflected light imaging [115] in a recognition context employing several classical recognition toolchains (for most configurations the reflected light approach was superior due to the more uniform illumination—light intensity varies more due to changing thickness of the tissue layers in transillumination). With respect to preprocessing, [316] propose a combination of high-frequency emphasis filtering and histogram equalisation, which has also been successfully applied to finger vein data [114].
Concerning feature extraction, Table 1.4 provides an overview of the existing techniques. We first discuss techniques relying on the extracted vascular structure. Lee et al. [143] use a directional filter bank involving different orientations to extract vein patterns, and the minimum directional code is employed to encode line-based vein features into a binary code. Explicit background treatment is applied similar to the techniques used in [277] for palm veins. The knuckle tips are used as key points for the image normalisation and extraction of the RoI [131]. Comparison scores are generated by a hierarchical comparison score from the four topologies of triangulation in the binarised vein structures, which are generated by Gabor filtering.
Classical vessel minutiae are used as features in [271], while [33] adds dynamic pattern tree comparison to accelerate recognition performance to the minutiae representation. A fixed-length minutiae-based representation originating from fingerprint biometrics, i.e. spectral minutiae [82], is applied successfully to represent dorsal hand vein minutiae in a corresponding recognition scheme. Biometric graph comparison, as already described before in the context of other vascular modalities, is also applied to graphs constructed from skeletonised dorsal hand vascular networks. Zhang et al. [310] extend the basic graph model consisting of the minutiae of the vein network and their connecting lines to a more detailed one by increasing the number of vertices, describing the profile of the vein shape more accurately. PCA features of patches around minutiae are used as templates in this approach, and thus this is an approach combining vascular structure information with local texture description. This idea is also followed in [93], however, employing different technologies: A novel shape representation methodology is proposed to describe the geometrical structure of the vascular network by integrating both local and holistic aspects and finally combined with LPB texture description. Also, [307] combine geometry and appearance methods and apply these to the Bosphorus dataset which is presented the first time in this work. [86] use an ICA representation of the vascular network obtained by thresholding-based binarisation and several post-processing stages.
Texture-oriented feature extraction techniques are treated subsequently. Among them, again key point-based schemes are the most prominent option. A typical toolchain description including the imaging device used, image processing methods proposed for geometric correction, region of interest extraction, image enhancement and vein pattern segmentation, and finally the application of SIFT key point extraction and comparison with several enrollment samples is described in [267]. Similarly, [150] uses contrast enhancement with subsequent application of SIFT in the comparison stage. Hierarchical key points’ selection and mismatch removal is required due to excessive key point generation caused by the enhancement procedure. SIFT with improved key point detection is proposed [262] as the NIR dorsal hand images do not contain many key points. Also, an improved comparison stage is introduced as compared to traditional SIFT key point comparison. Another approach to improve the key point detection stage is taken by [311], where key points are randomly selected and using SIFT descriptors an improved, fine-grained SIFT descriptor comparison is suggested. Alternatively, [249] conduct key point detection by Harris-Laplace and Hessian-Laplace detectors and SIFT descriptors, and corresponding comparison is applied. [270] propose a fusion of multiple sets of SIFT key points which aims at reducing information redundancies and improving the discrimination power, respectively. Different types of key points are proposed to be used by [92], namely, based on Harris corner-ness measurement, Hessian blob-ness measurement and detection of curvature extrema by operating the DoG detector on a human vision inspired image representation (so-called oriented gradient maps).
Also, other types of texture descriptors have been used. A custom acquisition device and LBP feature description is proposed in [268]. Gabor filtering using eight encoding masks is proposed [168] to extract four types of features, which are derived from the magnitude, phase, real and imaginary components of the dorsal hand vein image after Gabor filtering, respectively, and which are then concatenated into feature histograms. Block-based pattern comparison introduced with a Fisher linear discriminant adopts a “divide and conquer” strategy to alleviate the effect of noise and to enhance the discriminative power. A localised (i.e. block-based) statistical texture descriptor denoted as “Gaussian membership function” is employed in [28]. Also, classical CNN architectures have been suggested for feature extraction [144].
Dual-view acquisition has been introduced [215, 216, 315] resulting in a 3D point cloud representations of hand veins. Qi et al. [215, 216] propose a 3D point cloud registration for multi-pose acquisition before the point cloud matching vein recognition process based on a kernel correlation method. In [315], both the 3D point clouds of hand veins and knuckle shape are obtained. Edges of the hand veins and knuckle shape are used as key points instead of other feature descriptors because they are representing the spatial structure of hand vein patterns and significantly increase the amount of key points. A kernel correlation analysis approach is used to register the point clouds.
Multimodal fusion techniques have been used, e.g. [86] use dorsal hand veins as well as palm veins while [28] fuse palmprint, palm–phalanges print and dorsal hand vein recognition. The knuckle tips have been used as key points for the image normalisation and extraction of region of interest in [131]. The comparison subsystem combines the dorsal hand vein scheme [131] and the geometrical features consisting of knuckle point perimeter distances in the acquired images.
3.4 Wrist Vein Recognition Toolchain
There are no specific review articles on wrist vein recognition, but [151, 275] review work on various types of hand-based vein recognition techniques. Overall, the literature on wrist vein recognition is sparse. A low-cost device to capture wrist vein data is introduced [195] with good results when applying standard recognition techniques to the acquired data as described subsequently. Using vascular pattern-related feature extraction, [177] propose the fusion of left and right wrist data; a classical preprocessing cascade is used and binary images resulting from local and global thresholding are fused for each hand. A fast computation of cross-correlation comparison of binary vascular structures with shift compensation is derived in [186]. Another low-cost sensor device is proposed in [221]. Experimentation with the acquired data reveals Log Gabor filtering and a sparse representation classifier to be the best of 10 considered techniques. The fixed-length spectral minutiae representation has been identified to work well on minutiae extracted from the vascular pattern [82].
With respect to texture-oriented feature representation, [49] employs a preprocessing consisting of adaptive histogram equalisation and enhancement using a discrete Meyer wavelet. Subsequently, LBP is extracted from patches with subsequent BoF representation in a spatial pyramid.
3.5 Retina Recognition Toolchain
Survey-type contributions on retina recognition can be found in [97, 166] where especially the latter manuscript is a very recent one. Fundus imagery exhibits very different properties as compared to the sample data acquired from hand-related vasculature as shown in Fig. 1.4a. In particular, the vascular network is depicted with high clarity and with far more details with respect to the detailed representation of fine vessels. As the vessels are situated at the surface of the retina, illumination does not have to penetrate tissue and thus no scattering is observed. This has significant impact on the type of feature representations that are mainly used—as the vascular pattern can be extracted with high reliability, the typical features used as templates and in biometric comparisons are based on vascular minutiae. On the other hand, we hardly see texture-oriented techniques being applied. With respect to alignment, only rotational compensation needs to be considered, in case the head or the capturing instrument (in case of mobile capturing) is being rotated. Interestingly, retina recognition is not limited to the authentication of human beings. Barron et al. [15] investigate retinal identification of sheep. The influence of lighting and different human operators is assessed for a commercially available retina biometric technology for sheep identification.
As fundus imaging is used as an important diagnostic tool in (human) medicine (see Sect. 1.8), where the vascular network is mainly targeted as the entity diagnosis is based on, a significant corpus of medical literature exists on techniques to reliably extract the vessel structure (see [260] for a performance comparison of publicly available retinal blood vessel segmentation methods). A wide variety of techniques has been developed, e.g.
-
Wavelet decomposition with subsequent edge location refinement [12],
-
2-D Gabor filtering and supervised classification of vessel outlines [241],
-
Ridge-based vessel segmentation where the direction of the surface curvature is estimated by the Hessian matrix with additional pixel grouping [245],
-
Frangi vessel enhancement in a multiresolution framework [26],
-
Application of matched filters, afterwards a piecewise threshold probing for longer edge segments is conducted on the filter responses [90],
-
Neural network-based pixel classification after application of edge detection and subsequent PCA [240],
-
Laplace-based edge detection with thresholding applied to detected edges followed by a pixel classification step [259],
-
Wavelet transform and morphological post-processing of detail sub-band coefficients [137], and
-
Supervised multilevel deep segmentation networks [180].
Also, the distinction among arterial and venous vessels in the retina has been addressed in a medical context [95], which could also exploited by using this additional label in vascular pattern comparison.
When looking at techniques for the recognition toolchain, one of the exceptions not relying on vascular minutiae is represented by an approach relying on Hill’s algorithm [25] in which fundus pixels are averaged in some neighbourhood along scan circles, typically centred around the blind spot. The resulting waveforms (extracted from the green channel) are contrast-enhanced and post-processed in Fourier space. Combining these data for different radii lead to “retina codes” as described in [67]. Another texture-oriented approach [169] applies circular Gabor filters and iterated spatial anisotropic smoothing with subsequent application of SIFT key point detection and matching. A Harris corner detector is used to detect feature points [54], and phase-only correlation is used to determine and compensate for rotation before comparing the detected feature points.
All the techniques described in the following rely on an accurate determination of the vascular network as first stage. In a hybrid approach, [261] combine vascular and non-vascular features (i.e. texture–structure information) for retina-based recognition. The entire retinal vessel network is extracted, registered and finally subject to similarity assessment [85], and a strong focus on a scale, rotation and translation compensating comparison of retinal vascular network is set by [127]. In [13], an angular and radial partitioning of the vascular network is proposed where the number of vessel pixels is recorded in each partition and the comparison of the resulting feature vector is done in Fourier space. In [66], retinal vessels are detected by an unsupervised method based on direction information. The vessel structures are co-registered via a point set alignment algorithm and employed features also exploit directional information as also used for vessel segmentation. In [182], not the vessels but the regions surrounded by vessels are used and characterised as discriminating entities. Features of the regions are compared, ranging from simple statistical ones to more sophisticated characteristics in a hierarchical similarity assessment process.
All subsequent techniques rely on the extraction of retinal minutiae , i.e. vessel bifurcations, crossings and endings, respectively. In most cases, the vascular pattern is extracted from the green channel after some preprocessing stages, with subsequent scanning of the identified vessel skeleton for minutiae [145, 191, 285] and a final minutiae comparison stage. An important skeleton post-processing stage is the elimination of spurs, breakages and short vessels as described in [61]. The pure location of minutiae is augmented by also considering relative angles to four neighbouring minutiae in [207]. Biometric Graph Matching, relying on the spatial graph connecting two vessel minutiae points by a straight line of certain length and angle, has also been applied to retinal data [134]. In [22], only minutiae points from major blood vessels are considered (to increase robustness). Features generated from these selected minutiae are invariant to rotation, translation and scaling as inherited from the applied geometric hashing. A graph-based feature points’ comparison followed by pruning of wrongly matched feature points is proposed in [190]. Pruning is done based on a Least-Median-Squares estimator that enforces an affine transformation geometric constraint.
The actual information content of retinal data has been investigated in some detail [232], with particular focus set on minutiae-type [103, 232] and vessel-representation-type templates [7], respectively.
3.6 Sclera Recognition Toolchain
An excellent survey of sclera recognition techniques published up to 2012 can be found in [44]. Sclera recognition is the most difficult vascular trait as explained subsequently. While imaging can be done with traditional cameras, even from a distance and on the move, there are distinct difficulties in the processing toolchain: (i) sclera segmentation involves very different border types and non-homogeneous texture and is thus highly non-trivial especially when considering off-angle imagery and (ii) the fine-grained nature of the vascular pattern and its movement in several layers when the eye is moving makes feature extraction difficult in case of sensitivity against these changes. As a consequence, rather sophisticated and involved techniques have been developed and the recognition accuracy, in particular under unconstraint conditions, is lower as compared to other vascular traits. Compared to other vascular traits, a small number of research groups have published on sclera recognition only. This book contains a chapter on using deep learning techniques in sclera segmentation and recognition, respectively [229].
A few papers deal with a restricted part of the recognition toolchain. As gaze detection is of high importance for subsequent segmentation and the determination of the eventual off-angle extent, [3] cover this topic based on the relative position of iris and sclera pixels. This relative position is determined on a scan line connecting the two eye corners. After pupil detection, starting from the iris centre, flesh-coloured pixels are scanned to detect eyelids. Additionally, a Harris corner detector is applied and the centroid of detected corners is considered. Fusing the information about corners and flesh-coloured pixels in a way to look for the points with largest distance to the pupil leads to the eye corners.
Also, sclera segmentation (as covered in the corresponding challenges/competitions, see Sect. 1.4) has been investigated in isolated manner. Three different feature extractors, i.e. local colour-space pixel relations in various colour spaces as used in iris segmentation, Zernike moments, and HOGs, are fused into a two-stage classifier consisting of three parallel classifiers in the first stage and a shallow neural net as second stage in [217]. Also, deep-learning-based semantic segmentation has been used by combining conditional random fields and a classical CNN segmentation strategy [170].
Subsequent papers comprise the entire sclera recognition toolchain. Crihalmeanu and Ross [37] introduce a novel algorithm for segmentation based on a normalised sclera index measure. In the stage following segmentation, line filters are used for vessel enhancement before extracting SURF key points and vessel minutiae. After multiscale elastic registration using these landmarks, direct correlation between extracted sclera areas is computed as biometric comparison. Both [2, 4] rely on gaze detection [3] to guide the segmentation stage, which applies a classical integro-differential operator for iris boundary detection, while for the sclera–eyelid boundary the first approach relies on fusing a non-skin and low saturation map, respectively. After this fusion, which involves an erosion of the low saturation map, the convex hull is computed for the final determination of the sclera area. The second approach fuses multiple colour space skin classifiers to overcome the noise factors introduced through acquiring sclera images such as motion, blur, gaze and rotation. For coping with template rotation and distance scaling alignment, the sclera is divided into two sections and Harris corner detection is used to compute four internal sclera corners. The angles among those corners are normalised to compensate for rotation, and the area is resized to a normalised number of pixels. For feature extraction, CLAHE enhancement is followed by Gabor filtering. The down-sampled magnitude information is subjected to kernel Fisher discriminant analysis, and the resulting data are subjected to Mahalanobis cosine similarity determination for biometric template comparison. Alkassar et al. [5] set the focus on applying sclera recognition on the move at a distance by applying the methodology of [2, 4] to corresponding datasets. Fuzzy C-means clustering sclera segmentation is proposed by [43]. For enhancement, high-frequency emphasis filtering is done followed by applying a discrete Meyer wavelet filtering. Dense local directional patterns are extracted subsequently and fed into a bag of features template construction. Also, active contour techniques have been applied in the segmentation stage as follows. A sclera pixel candidate selection is done after iris and glare detection by looking for pixels which are of non-flesh type and exhibit low saturation. Refinement of sclera region boundaries is done based on Fourier active contours [322]. A binary vessel mask image is obtained after Gabor filtering of the sclera area. The extracted skeleton is used to extract data for a line descriptor (using length and angle to describe line segments). After sclera region registration using RANSAC, the line segment information is used in the template comparison process. Again, [6] use the integro-differential operator to extract the iris boundary. After a check for sufficient sclera pixels (to detect eventually closed eyes) by determination of the number of non-skin pixels, an active contours approach is used for the detection of the sclera-eyelid boundary. For feature extraction, Harris corner and edge detections are applied and the phase of Log Gabor filtering of a patch centred around the Harris points is used as template information. For biometric comparison, alignment is conducted to the centre of the iris and by applying RANSAC to the Harris points.
Oh et al. [188] propose a multi-trait fusion based on score-level fusion of periocular and binary sclera features, respectively.
4 Datasets, Competitions and Open-Source Software
4.1 Hand-Based Vascular Traits
Finger vein recognition has been the vascular modality that has been researched most intensively in the last years, resulting in the largest set of public datasets available for experimentation and reproducible research as displayed in Table 1.5. The majority is acquired in palmar view, but especially in more recent years also dorsal view is available. All datasets are imaged using the transillumination principle. As a significant limitation, the largest number of individuals that is reflected in all these datasets is 610 (THU-FVFDT), while all the others do not even surpass 156 individuals. This is not enough for predicting behaviour when applied to large-scale or even medium-scale populations.
There are also “Semi-public” datasets, i.e. these can only be analysed in the context of a visit at the corresponding institutions, including GUC45 [81], GUC-FPFV-DB [225] and GUC-Dors-FV-DB [219] (where the former are palmar and the latter is a dorsal dataset, respectively). A special case is the (large-scale) datasets of Peking University, which are only partially available, but can be interfaced by the RATEFootnote 6 (Recognition Algorithm Test Engine), which has also been used in the series of (International) Finger Vein Recognition Contests (ICFVR/FVRC/PFVR) [281, 282, 303, 312]. This series of contests demonstrated the advances made in this field, e.g. the winner of 2017 improved the EER from 2.64 to 0.48% compared to the winner of 2016 [312].
The datasets publicly available for hand vein recognition are more diverse as shown in Table 1.6. Palmar, dorsal and wrist datasets are available, and we also find reflected light as well as transillumination imaging being applied. However, again, the maximal number of subjects covered in these datasets is 110, and thus the same limitations as with finger vein data do apply.
VeinPLUS [73] is a semi-public hand vein dataset (reflected light and transillumination, resolution of \(2784\times 1856\) pixels with RoI of \(500\times 500\) pixels). To the best of the authors’ knowledge, no public open competition has been organised in this area.
4.2 Eye-Based Vascular Traits
For retina recognition, the availability of public fundus image datasets is very limited as shown in Table 1.7. Even worse, there are only two datasets (i.e. VARIA and RIDB) which contain more than a single image per subject. The reason is that the other datasets originate from a medical background and are mostly used to investigate techniques for vessel segmentation (thus, the availability of corresponding segmentation ground truth is important). The low number of subjects (20 for RIDB) and low number of images per subjects (233 images from 139 subjects for VARIA) makes the modelling of intra-class variability a challenging task (while this is not possible at all for the medical datasets, for which this has been done by introducing distortions to the images to simulate intra-class variability [67]).
The authors are not aware of any open or public competition for retina biometrics.
For sclera-based biometrics, sclera segmentation (and recognition) competitions have been organised 2015–2018Footnote 7 (SSBC’15 [45], SSRBC’16 [46], SSERBC’17 [48], SSBC’18 [47]) based on the SSRBC Dataset (2 eyes of 82 individuals, RGB, 4 angles) for which segmentation ground truth is being prepared. However, this dataset is not public and only training data are made available to participants of these competitions. Apart from this dataset, no dedicated sclera data are available and consequently, most experiments are conducted on the VIS UBIRIS datasets: UBIRIS v1 [201] and UBIRIS v2 [202].
Synthetic sample data has been generated for several biometric modalities including fingerprints (generated by SFinGe [160] and included as an entire synthetic dataset in FVC2004 [159]) and iris (generated from iris codes using genetic algorithms [69] or entirely synthetic [38, 327]), for example. The background is to generate (large-scale) realistic datasets without the requirements of human enrollment avoiding all eventual pitfalls with respect to privacy regulations and consent forms. Also, for vascular structures, synthetic generation has been discussed and some interesting results have been obtained. The general synthesis of blood vessels (more from a medical perspective) is discussed in [276] where Generative Adversarial Networks (GANs) are employed. The synthesis of fundus imagery is discussed entirely with a medical background [24, 36, 64, 75] where again the latter two papers rely on GAN technology. Within the biometric context, finger vein [87] as well as sclera [42] data synthesis has been discussed and rather realistic results have been achieved.
Open-source or free software is a scarce resource in the field of vascular biometrics, a fact that we aim to improve on with this book project. In the context of the (medical) analysis of retinal vasculature, retinal vessel extraction software based on wavelet-domain techniques has been provided: The ARIA Matlab package based on [12] and a second MATLAB software package termed mlvesselFootnote 8 based on the methods described in [241].
For finger vein recognition, B. T. TonFootnote 9 provides MATLAB implementations of Repeated Line Tracking [174], Maximum Curvature [175], and the Wide Line Detector [94] (see [255] for results) and a collection of related preprocessing techniques (e.g. region detection [139] and normalisation [94]). These implementations are the nucleus for both of the subsequent libraries/SDKs.
The “Biometric Vein Recognition LibraryFootnote 10” is an open-source tool consisting of a series of plugins for bob.bio.base, IDIAP’s open-source biometric recognition platform. With respect to (finger) vein recognition, this library implements Repeated Line Tracking [174], Maximum Curvature [175] and the Wide Line Detector [94], all with the Miura method used for template comparison. For palm vein recognition,Footnote 11 a local binary pattern-based approach is implemented.
Finally, the “PLUS OpenVein Finger- and Hand-Vein SDKFootnote 12” is currently the largest open-source toolbox for vascular-related biometric recognition and is a feature extraction and template comparison/evaluation framework for finger and hand vein recognition implemented in MATLAB. A chapter in this book [116] is dedicated to a detailed description of this software.
5 Template Protection
Template protection schemes are of high relevance when it comes to the security of templates in biometric databases, especially in case of database compromise. As protection of biometric templates by classical encryption does not solve all associated security concerns (as the comparison has to be done after the decryption of templates and thus, these are again exposed to eventual attackers), a large variety of template protection schemes has been developed. Typically, these techniques are categorised into Biometric Crypto Systems (BCS), which ultimately target on the release of a stable cryptographic key upon presentation of a biometric trait and Cancelable Biometrics (CB), where biometric sample or template data are subjected to a key-dependent transformation such that it is possible to revoke a template in case it has been compromised [227]. According to [99], each class of template protection schemes can be further divided into two subclasses. BCS can either be key binding (a key is obtained upon presentation of the biometric trait which has before been bound to the biometric features) or key generating (the key is generated directly from the biometric features often using informed quantisation techniques). CB (also termed feature transformation schemes) can be subdivided into salting and non-invertible transformations [99]. If an adversary gets access to the key used in the context of the salting approach, the original data can be restored by inverting the salting method. Thus, the key needs to be handled with special care and stored safely. This drawback of the salting approaches can be solved by using non-invertible transformations as they are based on the application of one-way functions which cannot be reversed. In this handbook, two chapters are devoted to template protection schemes for finger vein recognition [121, 129] and both fall into the CB category.
Vein-based biometrics subsumes some of the most recent biometric traits. It is therefore not surprising that template protection ideas which have been previously developed for other traits are now being applied to vascular biometric traits, without developments specific for the vascular context. For example, in case we consider vascular minutiae points as features, techniques developed for fingerprint minutiae can be readily applied, like the fuzzy vault approach or techniques relying on fixed-length feature descriptors like spectral minutiae and minutiae cylinder codes. In case binary data representing the layout of the vascular network are being used as feature data, the fuzzy commitment scheme approach is directly applicable.
5.1 Hand-Based Vascular Traits
Starting the discussion with finger vein recognition, we find classical signal-domain CB schemes being applied, like block re-mapping and image warping [199]. Spectral minutiae representations [82] are subjected to binarisation and subsequently fed into Bloom filters to result in a CB scheme, thereby avoiding position correction during template comparison as required by many techniques based on vascular structure representation [71]. We find techniques, which apply both CB and BCS: After applying a set of Gabor filters for feature extraction and subsequent dimensionality reduction using PCA, a CB scheme close to Bio-Hashing is used employing random projections. The obtained coefficients are binarised and subjected to a Fuzzy Commitment Scheme (FCS), which is a particular CBS approach based on helper data. This approach is used to secure medical data on a smart card [294]. A second approach combining CB and BCS is suggested in [296], where bio-hashing is applied to features generated by applying Gabor filters and subsequent LDA. The binary string is then subjected to FCS and also to a fuzzy vault scheme (where the binary string is somewhat artificially mapped into points used in the vault). Another approach to combine CB and BCS is proposed in [149], where finger vein minutiae are extracted and random projections are used to achieve revocability and dimensionality reduction. Afterwards, a so-called deep belief network architecture learns irreversible templates. Minutiae-based feature representations suffer from the drawback that they are no fixed-length representations (which is a prerequisite for the application of several template protection schemes)—techniques developed in the context of fingerprint minutiae representations have been transferred to vein minutiae representations, i.e. vein minutiae cylinder codes [84] and vein spectral minutiae representations [82].
A direct application of FCS to finger vein binary data is demonstrated in [83]. In a similar approach, [63] also apply the FCS, but they tackle the issue of bias in the binary data (as non-vein pixels are in clear majority as compared to vein pixels) by applying no vein detection but a simple thresholding scheme using the median. For FCS error correction, this approach applies product codes. A BCS approach based on quantisation is proposed in [278]: Based on multiple samples per subject (i.e. class), features with low intra-class scatter and high inter-class scatter (found by Fisher Discriminant Analysis (FDA)) are generated, which are finally subjected to a quantisation-based key generation where the quantisation parameters (helper data) depend on the distribution of the generated stable features. Another quantisation-based BCS is proposed in [29], where vein intersection points are located by considering a neighbourhood connectivity criteria, after Gabor-based enhancement with subsequent thresholding. However, the generation of a stable key is not discussed as it is just suggested to use a subset of the identified feature points as key material.
A multimodal CB scheme combining fingerprint and finger vein features uses a minutiae-based fingerprint feature set and an image-based finger vein feature set (obtained after Gabor filtering and subsequent application of LDA) [295]. Those features are fused in three variants and subjected to bio-hashing. An enhanced partial discrete Fourier transform (EP-DFT, omitting key-controlled parts of the DFT transform matrix) ensures non-invertability of the transform.
For palm vein recognition, in [34], palmprint templates are hashed with a set of pseudo-random keys to obtain a unique code called palmhash (basically the CB bio-hashing approach). FDA is applied to palm vein images; the FDA data are projected to a randomly generated orthogonal basis (Gram-Schmidt orthogonalisation) and subsequent thresholding results in a binary vector. A template-free key generation framework is suggested in [80], where local derivative patterns are used for feature extraction and a quantisation-based approach is used to generate keys, although a sufficiently detailed description is missing. An alternative approach being discussed is based on PalmSecure templates, which are processed in cooperation with iCognize GmbH. In [200], the palm vein data itself act as a key to encrypt a template database of independent biometric traits—however, no information about used vein features or how stability is achieved is given.
A multimodal template protection approach involving both hand and palm vein data suggests to fuse feature sets of both modalities [135, 136] (where stable vein points extracted from multiple enrollment samples act as feature sets) to create a fuzzy vault where chaff points are added as in the original scheme. However, the use of dual encryption involving both AES and DES in the second paper remains entirely unclear.
5.2 Eye-Based Vascular Traits
For eye-based vascular traits, not many template protection schemes have been proposed so far. For retina recognition, [167] applies a fuzzy vault scheme to secure retina minutiae. To account for weaknesses revealed in the fuzzy vault scheme due to non-uniformities in biometric data, a two-factor authentication is proposed using an additional password, to harden the BCS. In [192], minutiae of retina vessels are transformed in polar representation which have been computed from the gradient of intensity and eigenvalues of second-order derivatives. A quantisation-based BCS is applied to have only a single minutia in a spatial tile. These data are used as an encryption key, while the template is a random nonce—the encrypted data are generated by applying the quantised polar minutiae data as the key.
In the context of sclera recognition, [189] proposes a CB scheme based on a region indicator matrix which is generated using an angular grid reference frame. For binary feature template generation, a random matrix and a Local Binary Pattern (LBP) operator are utilised. Subsequently, the template is manipulated by user-specific random sequence attachment and bit shifting which enables normalised Hamming distance comparison to be used in the comparison stage.
6 Presentation Attacks and Detection, and Sample Quality
6.1 Presentation Attack Detection
One advantage of hand-based veins over other biometric traits is the fact that they are embedded inside the human body, as opposed to traits like fingerprints or faces. Moreover, vein images cannot be acquired from a distance without the subject noticing the capturing process. However, despite the claims of being resistant against inserting artefacts into the sensor to mimic real users, vein-based authentication turned out to be vulnerable to Presentation Attacks (PA) (experimentally shown using printed artefacts [252, 254]). Also, [27] presents some examples of how to produce spoofing artefacts for a dorsal hand vein scanner, however, without giving any quantitative results. Still, this work is the first one addressing this issue.
These demonstrated attacks triggered work on PA Detection (PAD) techniques and consequently in 2015, the first competition on countermeasures to finger vein spoofing attacks took place [253] (providing the IDIAP finger vein Spoofing-Attack Finger Vein Database consisting of real and fake finger vein images). The competition baseline algorithm looks at the frequency domain of finger vein images, exploiting the bandwidth of the vertical energy signal of real finger vein images, which is different for fakes ones. Three teams participated in this competition. The first team (GUC) uses Binarised Statistical Image Features (BSIF) [253]. They represent each pixel as a binary code. This code is obtained by computing the pixel’s response to a filter that is learned using statistical properties of natural images [253]. The second team (B-Lab) uses monogenic-scale space-based global descriptors employing the Riesz transform. This is motivated by the fact that local object appearance and shape within an image can be represented as a distribution of local energy and local orientation information. The best approach (team GRIP-PRIAMUS) utilises local descriptors, i.e. Local Binary Patterns (LBP), and Local-Phase Quantisation (LPQ) and Weber Local Descriptors (WLD). They distinguish between full and cropped images. LBPs and LPQ/WLD are used to classify full and cropped images, respectively.
However, countermeasures to finger vein PA were/are already developed prior or independent to this competition. In 2013, the authors of [183] introduced a fake finger vein image detection based upon Fourier, and Haar and Daubechies wavelet transforms. For each of these features, the score of spoofing detection was computed. To decide whether a given finger vein image is fake or real, an SVM was used to combine the three features.
The authors of [251] propose windowed dynamic mode decomposition (W-DMD) to be used to identify spoofed finger vein images. DMD is a mathematical method to extract the relevant modes from empirical data generated by non-linear complex fluid flows. While DMD is classically used to analyse a set of image sequences, the W-DMD method extracts local variations as low-rank representation inside a single still image. It is able to identify spoofed images by capturing light reflections, illuminations and planar effects.
Texture-based PAD techniques have been proven to be applicable to the imagery in the FV-Spoofing-Attack database [253] independent of the above-referenced competition, in particular, baseline LBP [220]. Inspired by the success of basic LBP techniques [181, 253] in finger vein PAD and the availability of a wide variety of LBP extensions and generalisations in the literature, [123] has empirically evaluated different features obtained by using these more recent LBP-related feature extraction techniques for finger vein spoofing detection. Additionally, the steerable pyramid is used to extract features subsequently used for FV spoofing detection [220].
Steerable pyramids are a set of filters in which a filter of arbitrary orientation is synthesised as a linear combination of a set of basis functions. This enables the steerable pyramids scheme to compute the filter response at different orientations. This scheme shows consistent high performance for the finger vein spoofing detection problem and outperforms many other texture-classification-based techniques. The approach is compared to techniques from [252], including two LBP variants, and to quality-based approaches computing block-wise entropy, sharpness and standard deviation. Qiu et al. [213] employ total variation regularisation to decompose original finger vein images into structure and noise components, which represent the degrees of blurriness and the noise distribution. Subsequently, a block local binary pattern descriptor is used to encode both structure and noise information in the decomposed components, the histograms of which are fed into an SVM classifier.
Finally, image quality measures have been proposed for finger vein PAD. A detection framework based on Singular Value Decomposition (SVD) is proposed in a rather confused paper [181]. The authors utilise the fact that one is able to extract geometrical finger edge information from infrared finger images. Finger vein images are classified based on Image Quality Assessment (IQA) without giving any clear indication about the actual IQA used and any experimental results. In [21], the authors successfully apply general-purpose non-reference image quality metrics to discriminate real finger vein images from fake ones. Subsequent work [242] additionally applies natural scene statistics and looks into the issue of cross-sensor and cross-subject finger vein presentation attack detection. However, it is often cumbersome to identify and/or design texture descriptors suited for a specific task in this context. As a consequence, generative techniques like deep learning employing Convolutional Neural Networks (CNNs) have been successfully applied to discriminate real from spoofed biometric finger vein data [185, 214, 223, 224].
In contrast to all finger vein PAD techniques reviewed so far (which are based on still images and exploit corresponding texture properties), [27] already realise that analysing single still images is not able to exploit liveness signs. Thus, in this work, it is suggested to look into differences of features in adjacent frames, however, without giving any concrete features or experimental results. A custom-designed 2D transillumination NIR-laser scanner [142] is used for finger vein liveness detection based on extracting parameters from laser speckle image sequences (e.g. average speckle intensity). The technique proposed by [218] aims also at liveness detection and relies on LED-NIR video data. In this approach, motion magnification is employed to magnify the subtle motion of finger veins caused by blood flow. A motion magnitude derived from the optical flow between the first and the last frame in the captured video is used to determine liveness of the subject. This book contains a chapter [125] on using finger vein PAD to secure fingerprint sensors.
In addition to the publicly available IDIAP VERA Finger Vein Spoofing Database used in the competition mentioned above, we have another finger vein spoofing dataset available: The SCUT-SFVD: A Finger Vein Spoofing/Presentation Attack Database.Footnote 13
There is less work on PAD for hand vein-based systems. PCA and power spectrum estimation of an autoregressive model are used [269] to detect artefacts resulting from printouts and from wearing coloured gloves. A dorsal hand vein dataset with artefacts produced by acquiring vein imagery with a smartphone camera has been created where the smartphones’ display has been inserted into the sensor [196]. Histogram of Oriented Gradients (HOG) turned out to deliver good results for discriminating real from fake samples [20]. The same group has also established the PALMSpoof dataset including three different types of palm vein artefacts including such generated by display and print attacks. In [18], a noise residual image is obtained by subtracting the denoised image from the acquired image. The local texture features extracted from the noise residual image are then used to detect the presentation attack by means of a trained binary support vector machine classifier. Additionally, in [19], statistical features computed from the distributions of pixel intensities, sub-band wavelet coefficients, and the grey-level co-occurrence matrix are used to discriminate original and fake samples. In addition to these private PAD datasets, the publicly available IDIAP VERA Spoofing Palm Vein datasetFootnote 14 is available to assess PAD technology.
Liveness detection based on speckle analysis in retinal imagery is proposed in [235], but we actually doubt that there is really a corresponding realistic threat vector in retinal imaging (except for mobile self-capturing). For sclera-based recognition, neither PAD techniques nor liveness detection has been addressed so far.
6.2 Biometric Sample Quality—Hand-Based Vascular Traits
Biometric sample quality is important in many aspects. The probably most important application case is to request another sample data capturing in case sample quality turns out to be too low. Moreover, quality is important for various types of fusion approaches by rating authentication based on low-quality samples as less reliable. There are strong connections to presentation attacks, as the quality of PA artefacts is often questionable, as also illustrated by the use of quality measures to counter PA. ISO/IEC 29794 standard contains definitions for face, fingerprint and iris biometric sample quality. However, for vascular biometrics, no such standardisation exists yet. Thus, in the following, we review the available literature on this topic for vascular biometric traits. It is clear that quality assessment techniques applicable in the targeted biometric context need to be non-reference, i.e. without considering any “original” image in the assessment (as this original not even exists). An issue specific to vascular biometrics is the distinction among techniques being applied to the sample image as it is (we denote those as “a priori”) from techniques which analyse the vascular network after extraction (denoted as “a posteriori”, as for these techniques the vessels need to be segmented first, thus imposing significantly higher computational cost, and being feature extraction specific moreover).
We start the discussion by reviewing work on finger vein image quality assessment. A non-vein specific extension of SNR incorporating human visual system properties is proposed in [165] and combined with a contrast score and finger vein specific measures like area and finger shifting score [156]. It is not really obvious why the evaluation is done with respect to human inspection. Highly vein specific (and applicable in principle to most vein-based biometric traits) is a suggested quality measure based on the curvature in Radon space [212] (which is applied a priori), which is later combined with an assessment of connectivity, smoothness and reliability of the binary vein structures (applied a posteriori) [210]. Based on the NIR sample images, [305] use image contrast, information content and capacity to filter out low-quality finger vein images, and a very similar approach is taken by [291]. These entities are also combined in a fusion scheme termed “triangular norm” [198] combining these a priori measures into a single (weighted) one.
Another a posteriori approach is proposed by [283], in which, after extracting vessels using a Gabor filter, thick major vessels and short minor vessels construct the hierarchical structure of the finger vein network. This structure is modelled by a hierarchical Gaussian energy distribution which is used to assess the hierarchical quality of the vessel network. Also, [184] is based on an a posteriori approach, in which the quality of a finger vein image is measured by using the number of detected vein points in relation to the depth of the vein profile, which allows individual variations of vein density to be considered for quality assessment.
Learning-based schemes are employed to binary vessel structure images (so to be applied a posteriori) both by [321] and [208, 211], where the former is based on support vector regression and the latter on a CNN approach. Both approaches share the disadvantage of requiring a significant amount of (manually labelled) training data. A quality-driven fusion approach for vein structure and skin texture is suggested by [96].
For palm and hand vein image quality, respectively, the available literature is less extensive. However, most approaches suggested for finger vein quality assessment as discussed before can be transferred to palm and hand vein imagery. A fusion of clarity and brightness uniformity is suggested for palm vein data in [274]. Another quality notion for palm vein images [104], being much more specific, addresses one of the problems in contactless acquisition, i.e. the differences in camera–object distance and the resulting defocus blur. Corresponding quality is assessed by combining the Tenengrad sharpness measure [158] with a classical image quality metric (SSIM [265]), which is applied to pairs of images of different distances. Authors were able to show a clear relation of the assessment results with recognition accuracy. Natural scene statistics have also been used to assess the quality of palm vein imagery [272]. For dorsal hand vein images, [264] introduces a quality-specific vein recognition system, which uses the “CFISH score” in adaptively selecting LBP-based feature extraction according to high or low quality of the samples. The CFISH score is computed as weighted average from wavelet detail sub-bands’ mean energy and variance, thus representing image sharpness.
6.3 Biometric Sample Quality—Eye-Based Vascular Traits
In the context of retina images’ quality (quality of fundus images), work has exclusively been done in a medical context. Thus, it is important to discriminate among techniques addressing general quality (and thus potentially relevant for biometrics’ use) and techniques which specifically address quality related to the detection of certain diseases (which might not be suited in a biometric context). For example “..., an image with dark regions might be considered of good quality for detecting glaucoma but of bad quality for detecting diabetic retinopathy” [70]. However, it turns out that the quality measures considered are not really pathology-specific and could be all employed in retina biometrics in principle.
Without stating a clear diagnostic aim, local sharpness as well as illumination measures are combined into a four-stage measure [16] which has been validated on a ground truth provided by three ophthalmologists and three ophthalmic nurses with special training in and considerable experience of fundus photography, respectively.
In [70], fundus image quality is defined as “characteristics of an image that allow the retinopathy diagnosis by a human or software expert” (thus, it is focused on the vasculature of the retina). In this work, a thorough discussion of retina quality measures developed until 2009 is given. Authors propose a scale-invariant measure based on the density of extracted vessels; thus, it is only applicable after vascular structure has been detected (so it is an a posteriori measure). These features are combined with RGB histograms used in earlier work on retinal image quality. The work in [306], being quite similar, aims to determine, whether the quality of a retinal image is sufficient for computer-based diabetic retinopathy screening. Authors combine vessel density, histogram, co-occurrence matrix as well as local edge width and gradient magnitude-based features, respectively. Evaluation is done with respect to the ground truth (four quality grades) as provided by two optometrists.
As diagnostic aims, [197] define glaucoma and diabetic retinopathy. The proposed technique maps diagnosis-relevant criteria—inspired by diagnosis procedures based on the advise of an eye expert—to quantitative and objective features related to image quality. Independent from segmentation methods, global clustering and the consideration of inter-cluster differences are used to determine structural contrast which implies the recognisability of distinct anatomical structures. This measure is combined with local sharpness based on gradient magnitude and texture features (three Haralick features are used) for classification. Ground truth for quality staging is provided by three human observers including one eye expert.
In [257], first it is determined if the clinically most relevant area (the region around the macula) is distorted by areas of very dark and/or very light areas. Subsequently, if the image exhibits sufficient clinically relevant context, three different types of focus measures, i.e. wavelet-based ones, Chebyshev moment-based focus features, and a measure based on computing the difference between the original and a median-filtered version of the image, are fused into a common feature representation and classified (the Matlab Fuzzy Logic Toolbox is used).
Köhler et al. [124] present a quality metric to quantify image noise and blur and its application to fundus image quality assessment. The proposed metric takes the vessel tree visible on the retina (as determined by the Frangi’s vesselness criterion) as guidance to determine an image quality score. Vessel-containing patches are weighted more strongly in this scheme. The performance of this approach is demonstrated by correlation analysis with the full-reference metrics Peak-Signal-to-Noise Ratio (PSNR) and structural similarity (SSIM) for artificially degraded data. For real data, the metric correlates reasonably to a human observer. Finally, a deep learning framework has been applied recently to train a network [230] to rate fundus images into “accept” and “reject” classes, based on a set of 3428 fundus images labelled correspondingly by three human experts and evaluated on 3572 other images leading to perfect separation.
For sclera image quality grading, the major focus of work done so far is on image sharpness/edge clarity. After a blink detection approach based on a Sobel filter, [324] evaluates the strength of responses to a spatial domain high-pass filter for the detection of blurred images, while [5] introduces a four-class quality grading scheme based on the response to a Laplacian edge operator. An a posteriori approach also involving segmentation and feature quality is introduced in [323].
7 Mobile and On-the-Move Acquisition
The application of biometric recognition systems in mobile scenarios and acquisition of sample data on-the-move raises some problems compared to the stationary use of such systems. This is true in general and thus also applies to vascular biometrics. First of all, mobile devices are typically restricted in terms of available resources, e.g. in terms of power provision and available computational capacity. Therefore, applied algorithms need to be low-cost and have to be executed on embedded systems typically. In addition, the acquisition process in both settings is more unconstrained (more degrees of freedom for the placement of the biometric trait and varying environmental conditions) compared to the stationary case, causing several recognition performance issues (see e.g. challenges in contactless hand vein systems [65, 109, 179]). Eventually, the authentication process is unsupervised, enabling presentation attacks [162]. Furthermore, the mobile system might not be a trusted platform, especially if the authentication is performed on the user’s smartphone. This opens the door for all kinds of insertion and replay attacks to the biometric system. Hence, there is a need for presentation attack detection systems as well as methods to prove the authenticity and integrity of the biometric sample that has been captured.
7.1 Hand-Based Vascular Traits
In medical imaging, vein visualisation using mobile devices is a current topic of research. In [106], the available technology for subcutaneous vein detection is reviewed and low-cost mobile health solution using near-infrared spectroscopy is proposed.
Several papers deal with low-power and low-complexity implementations without looking into the sample acquisition process. Thus, no mobile capturing is foreseen, and the focus is on an implementation potentially suited for a mobile deployment. A low-complexity finger vein recognition algorithm is reported to be implemented on a DSP platform [147], but while actual power consumption is reported, the actual DSP system is not revealed. A modified thermal webcam is used for image acquisition in the three papers subsequently listed. FPGA implementations of hand vein [58] as well as finger vein [117, 118] recognition algorithms are reported, where the latter paper uses an NIR LED array for transillumination imaging, while the other two use the same device for reflected light acquisition.
However, work has been done to develop custom devices for mobile vein capturing: A device almost the size of an SLR camera has been constructed which enables both fingerprint and finger vein capturing [140]. Also, the concept of using smartwatches or similar devices for vein capturing has been suggested, i.e. Samsung has presented an idea involving a smartwatch with built-in NIR illuminationFootnote 15 and associated capturing of dorsal hand veins, while the startup BioWatchIDFootnote 16 acquire wrist veins with their bracelet technology.
Of course, smartphones have been considered as potential authentication devices for hand-related vascular biometrics. However, we face significant challenges. First, smartphones typically do not operate in the NIR domain (although sensors are able to capture NIR rays). Second, smartphones do not offer NIR-type illumination required for reflected light illumination as well as transillumination. In the VIS domain, recent work [14] reports on using current smartphones to capture hand imagery and using geometrical features for authentication. While this does not seem to be possible for vein-related authentication, still we find work pointing into this direction. In fact, Hitachi Footnote 17 claims to be able to enable “high-precision finger vein authentication” based on the RGB images users take with their smartphone. Also, the mobile App VeinSeekFootnote 18 claims to emphasise vein structure using a common smartphone. Personal experience shows that some benefits can be observed for dorsal hand veins, while for palmar veins we were not able to observe a positive effect when using this tool. While the entire idea seems to be slightly obscure at first sight, there is indeed work [243] which explains RGB-based vein visualisation enhancement from RGB images by exact RGB reflection modelling, Wiener filtering and additional post-processing. However, this idea can be only applied to superficial vascular structures. Wrist vein recognition using VIS smartphone imagery is proposed in [132], where shallow neural network structures and PCA are applied to the RoI. However, experiments are restricted to a small dataset consisting of Caucasian ethnicity subjects only.
When looking at NIR smartphone-based capturing, there are different approaches to solve the issues discussed before. The first observation is that Fujitsu managed to minimise their PalmSecure sensor significantly, so that the F-pro sensor variant can be used as authentication device for the Fujitsu V535 tablet. Thus, we might expect the deployment of this sensor generation in smartphones. In the context of finger vein recognition, reflected light illumination has been investigated [308] as it is clear that transillumination cannot be implemented in smartphones. As expected, this illumination variant decreases the recognition accuracy for finger vein biometrics.
In a medical patient identification context, several variants to visualise dorsal hand veins have been investigated in [65]. In any case, external NIR illumination is used, image acquisition is done either with a smartphone (with NIR-blocking filter in place) or an external night-vision webcam used as a smartphone plug-in. Contrasting to this simple solution, a custom-built plug-on finger vein acquisition device [239] based on reflection-based imaging has been developed. Experimentation reveals rather low contrast, especially in difficult lighting conditions. An NIR illumination module attached to a smartphone with removed NIR-blocking filterFootnote 19 is proposed [53] to capture dorsal hand veins. In this context, the authors investigate challenge–response protocols based on pulsed illumination intensity changes to secure the capturing process against replay attacks.
Also, dedicated NIR-imaging smartphone prototypes (or components thereof) including NIR illumination have been developed. SONY already came up with a finger vein capturing smartphone in 2009 [231], while another research-oriented prototype has been presented 7 years later [17].
Finally, also 3D imaging was discussed to generate representations involving vessel structures. Simulating a corresponding smartphone depth sensor, a KinectV2 [319] has been used to capture the dorsal hand side to generate such datasets. However, the actual processing of the Kinect data and the conducted biometric comparisons are not described in sufficient detail. Last but not least, there are rumours that Apple might go for “Vein IDFootnote 20” for their next-generation iPhones, which could be based on depth sensing as well.
The only work suggesting a kind-of on-the-move acquisition for hand-related vascular technology is a prototype proposed by Hitachi [164], who introduce a finger vein device which captures five fingers concurrently using a kind of side transillumination, where the NIR rays not penetrating the fingers do not directly enter the camera system. The proposed system is said to operate in a walk-through style, while this is not entirely clear from the description.Footnote 21
7.2 Eye-Based Vascular Traits
For eye-based vascular biometric techniques, much less work can be identified. With respect to retina imaging, traditional fundus cameras are large, expensive stationary medical devices. Only recently, there is a trend to consider also mobile variants. A prototype of a handheld, portable fundus camera is introduced in [105], where also the quality of the acquired fundus images is compared to a standard, stationary device. A commercial solution following the same path is offered by OPTOMED.Footnote 22 While the latter devices require a person to operate the portable capturing device, [246] propose a self-capturing device providing user feedback to optimise the acquired data.
To reduce costs, also the use of smartphones in fundus imaging has been discussed (see [77] for an overview of corresponding ideas). A common approach is the manual positioning of a lens in front of eye and the subsequent capturing of the lens with a smartphone [119, 146]. More professional though is the direct attachment of an imaging device to the smartphone (which can be rather large [155]), an approach for which several commercial solutions do exists, e.g. as provided by VolkFootnote 23 or Remidio.Footnote 24 The D-EYE system excels by its small-scale device being magnetically attached to an iPhone.Footnote 25
It has to be noted that all these reported solutions for mobile fundus photography (i.e. retina capturing) have not been discussed in the context of retina biometrics but in the medical imaging context. Nevertheless, these developments could render retina biometrics less intrusive and thus more realistic. Capturing on-the-move can be ruled out for retina biometrics as the illumination of the retina requires a focused and precise illumination process.
Last but not least, in the context of sclera recognition, the topic of mobile capturing has not been sufficiently addressed yet. The only work in this direction that we are aware of [2] applies sclera segmentation and recognition technology to UBIRIS v2 [202] data and titles this work as “... Captured On-The-Move and At-A-Distance” as the UBIRIS v2 data have been captured under these conditions. However, it is out of question that sclera recognition can be performed on datasets acquired by common smartphones [5] (e.g. when focussing on MICHE I [50, 52] and MICHE II [51] datasets as done in [5]).
8 Disease Impact on Recognition and (Template) Privacy
This section is devoted to a relatively unexplored field. For other modalities, e.g. fingerprints, it is better known and documented that certain diseases [55] and different age groups [176, 256] impact on recognition performance.
For hand-based vascular biometric traits, knowledge about certain diseases which influence the vessels’ position and structure does exist [83], e.g. Arteriovenous Malformation (AVM) and the Hypothenar Hammer Syndrome (HHS). Also, it is known that certain injuries, including the insertion of small soft plastic tubes (Venflon) into venous vessels in the context of stationary medicamentation, can cause a change in the vessels’ layout and thickness. However, there is neither theoretical nor empirical evidence that these effects might or might not actually degrade vascular-based recognition performance.
For eye-based vascular biometric traits, the situation is somewhat similar, but the argumentation is more indirect. As there exist certain diseases which can be diagnosed from fundus imagery (see e.g. [41] for a survey including several diseases which obviously affect retinal vasculature like diabetic retinopathy) and sclera images ([56] reports a sclera-vessel-based screening for cardiovascular diseases), those diseases also could eventually impact on corresponding recognition accuracy. Also, in this area, there is no evidence in favour or against this hypothesis.
Extraction of privacy-related information from biometric templates is one of the main motivations to establish template protection schemes. For example, it is well known that gender information can be extracted from facial or gait-related biometric samples and even templates [74], also fingerprints are known to reveal gender information.Footnote 26 Other privacy-related attributes include age, ethnicity and of course various types of medically relevant information.
For vascular biometrics, corresponding research is in its infancy. The extent of privacy-threatening information that can be potentially extracted also significantly depends on the type of data to be analysed. If we consider sample data (which is hardly ever stored in an operational biometric system, at least not online, except for recent deep-learning-based schemes relying on assessment of sample data pairs or triples), the threat of extracting such information illegitimately is much higher compared to looking at templates. Also, for templates, a representation of the vascular network based on the binary structure reveals much more information compared to a minutiae-based or even texture-property-based representation.
Having discussed diseases affecting the vascular layout above, it is obvious that information about these diseases can/could/might be extracted from corresponding sample data or templates, respectively. For finger vein sample data, it has been additionally shown [39] that gender as well as 2–4 age classes can be determined with high accuracy (>95%) based on typical preprocessing and the application of LBP. For dorsal hand vein data, [273] reports that feature representation based on vessel structure, PCA, LBP and SIFT do not allow to correctly discriminate male and female subjects. However, the authors propose to apply a feature learning scheme based on an unsupervised sparse feature learning model and achieve a classification accuracy of up to 98%.
One important aspect to be considered in this area is the lack of public datasets with metadata suited for corresponding analyses as well as reproducible research work. This should be considered when establishing datasets in the future.
9 Conclusion and Outlook
The structure of human vasculature is a suited identifier to be used in biometric systems. Currently, we have seen exploitation of this observation in the context of hand- and eye-oriented vascular biometric recognition.
For the hand-oriented modalities (i.e. finger vein, palm vein, (dorsal) hand vein and wrist vein recognition), several undisputed advantages over fingerprint recognition do exist; however, we still see several open issues being present, also inhibiting further widespread deployments. For example, the promise of contactless operation has been made, but many current system (especially in finger vein recognition) users need to touch the capturing devices, often for good reasons. Furthermore, contrasting to other biometric modalities, current commercial sensors do not allow to output captured sample data, which prohibits further progress and open competition in the area. Potential users planning a deployment cannot rely on large-scale public evaluation of the technology, and they have to rely on data provided by the companies producing sensors and corresponding recognition software—public evaluation would certainly increase trust in this technology. Last but not least, there is a huge gap in the quality of extracted vascular structures comparing currently used biometric technology (reflected light or transillumination NIR imaging) and techniques that are used in medical imaging for similar purposes (e.g. magnetic resonance angiography or similar). Thus, a further increase in sample quality while keeping sensor costs low is still an important challenge.
For the eye-oriented modalities (i.e. retina and sclera recognition), future does not seem to be as promising as many obstacles still exist. Retina recognition suffers from the highly intrusive sample acquisition process (while the quality of the acquired vascular structures is the best of all vascular modalities considered, allowing for very accurate recognition) and the high cost of (medical) stationary sensors. Eventually, recent developments in mobile retina capturing might become game changers for this modality. Sclera recognition does not have obvious advantages as compared to face recognition in terms of applicability and security, and good quality sample data are difficult to acquire from a distance or on the move. Eventually, similar as for periocular recognition, there is potential to be employed in a multimodal setting of facial biometric characteristics, as acquisition can be done in the visible domain.
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
available on MATLAB Central: http://www.mathworks.nl/matlabcentral/fileexchange/authors/57311.
- 10.
- 11.
- 12.
- 13.
- 14.
- 15.
- 16.
- 17.
- 18.
- 19.
- 20.
- 21.
- 22.
- 23.
- 24.
- 25.
- 26.
References
Agha AMJ, George LE (2014) Palm veins recognition and verification system: design and implementation. LAP Lambert Academic Publishing, Germany
Alkassar S, Woo WL, Dlay SS, Chambers JA (2016) A novel method for sclera recognition with images captured on-the-move and at-a-distance. In: 4th International workshop on biometrics and forensics (IWBF’16), pp 1–6
Alkassar S, Woo WL, Dlay SS, Chambers JA (2016) Efficient eye corner and gaze detection for sclera recognition under relaxed imaging constraints. In: 24th European signal processing conference, EUSIPCO 2016, Budapest, Hungary, 29 Aug–2 Sept 2016, pp 1965–1969
Alkassar S, Woo WL, Dlay SS, Chambers JA (2016) Enhanced segmentation and complex-sclera features for human recognition with unconstrained visible-wavelength imaging. In: International conference on biometrics, ICB 2016, Halmstad, Sweden, 13–16 June 2016, pp 1–8
Alkassar S, Woo WL, Dlay SS, Chambers JA (2017) Sclera recognition: on the quality measure and segmentation of degraded images captured under relaxed imaging conditions. IET Biom 6(4):266–275
Alkassar S, Woo WL, Dlay SS, Chambers JA (2017) Robust sclera recognition system with novel sclera segmentation and validation techniques. IEEE Trans Syst Man Cybern: Syst 47(3):474–486
Arakala A, Culpepper JS, Jeffers J, Turpin A, Boztas S, Horadam KJ, McKendrick AM (2009) Entropy of the retina template. In: Advances in biometrics: international conference on biometrics (ICB’09), volume 5558 of Springer LNCS, pp 1250–1259
Arakala A, Davis S, Horadam KJ (2019) Vascular biometric graph comparison: theory and performance. In: Uhl A, Busch C, Marcel S, Veldhuis R (eds) Handbook of vascular biometrics. Springer Science+Business Media, Boston, MA, USA, pp 355–394
Arakala A, Hao H, Davis SA, Horadam KJ (2015) The palm vein graph—feature extraction and matching. In: ICISSP 2015—Proceedings of the 1st international conference on information systems security and privacy, ESEO, Angers, Loire Valley, France, 9–11 February, 2015, pp 295–303
Asaari MSM, Rosdi BA, Suandi SA (2014) Fusion of band limited phase only correlation and width centroid contour distance for finger based biometrics. Expert Syst Appl 41(7):3367–3382
Banerjee A, Basu S, Basu S, Nasipuri M (2018) ARTeM: a new system for human authentication using finger vein images. Multimed Tools Appl 77(5):5857–5884
Bankhead P, Scholfield CN, McGeown JG, Curtis TM (2012) Fast retinal vessel detection and measurement using wavelets and edge location refinement. PLoS ONE 7(3):e32435
Barkhoda W, Tab FA, Amiri MD, Nouroozzadeh M (2011) Retina identification based on the pattern of blood vessels using fuzzy logic. EURASIP J Adv Sig Proc 2011:113
Barra S, De Marsico M, Nappi M, Narducci F, Ricci D (2018) A hand-based biometric system in visible light for mobile environments. Inf Sci
Barron UG, Corkery G, Barry B, Butler F, McDonnell K, Ward S (2008) Assessment of retinal recognition technology as a biometric method for sheep identification. Comput Electron Agric 60(2):156–166
Bartling H, Wanger P, Martin L (2009) Automated quality assessment of digital fundus photography. Acta Ophthalmol 87(6):643–647
Bazrafkan S, Nedelcu T, Costache C, Corcoran P (2016) Finger vein biometric: smartphone footprint prototype with vein map extraction using computational imaging techniques. In: Proceedings of the IEEE international conference on consumer electronics (ICCE’16), pp 512–513
Bhilare S, Kanhangad V (2018) Securing palm-vein sensors against presentation attacks using image noise residuals. J Electron Imaging 27:053028
Bhilare S, Kanhangad V, Chaudhari N (2018) A study on vulnerability and presentation attack detection in palmprint verification system. Pattern Anal Appl 21(3):769–782
Bhilare S, Kanhangad V, Chaudhari N (2017) Histogram of oriented gradients based presentation attack detection in dorsal hand-vein biometric system. In: Fifteenth IAPR international conference on machine vision applications (MVA’17), pp 39–42
Bhogal APS, Söllinger D, Trung P, Hämmerle-Uhl J, Uhl A (2017) Non-reference image quality assessment for fingervein presentation attack detection. In: Proceedings of 20th Scandinavian conference on image analysis (SCIA’17), volume 10269 of Springer lecture notes on computer science, pp 184–196
Bhuiyan A, Akter Hussain Md., Mian AS, Wong TY, Ramamohanarao K, Kanagasingam Y (2017) Biometric authentication system using retinal vessel pattern and geometric hashing. IET Biom 6(2):79–88
Black S (2018) All that remains: a life in death. Doubleday
Bonaldi L, Menti E, Ballerini L, Ruggeri A, Trucco E (2016) Automatic generation of synthetic retinal fundus images: vascular network. Procedia Comput Sci 90:54–60. In: 20th Conference on medical image understanding and analysis (MIUA 2016)
Borgen H, Bours P, Wolthusen SD (2008) Visible-spectrum biometric retina recognition. In: International conference on intelligent information hiding and multimedia signal processing (IIH-MSP’08), pp 1056–1062
Budai A, Bock R, Maier A, Hornegger J, Michelson G (2013) Robust vessel segmentation in fundus images. Int J Biomed Imaging 2013:ID 154860
Cao G, Pan J, Qin B, Du G (2009) The anti-spoofing study of vein identification system. In: International conference on computational intelligence and security (CIS’09), vol 02, pp 357–360
Chaudhury G, Srivastava S, Bhardwaj S, Bhargava S (2016) Fusion of palm-phalanges print with palmprint and dorsal hand vein. Appl Soft Comput 47:12–20
Chavez-Galaviz J, Ruiz-Rojas J, Garcia-Gonzalez A (2015) Embedded biometric cryptosystem based on finger vein patterns. In: 12th International conference on electrical engineering, computing science and automatic control, CCE 2015, Mexico City, Mexico, 28–30 Oct 2015, pp 1–6
Chen C, Zhendong W, Zhang J, Li P, Azmat F (2017) A finger vein recognition algorithm based on deep learning. Int J Embed Syst 9(3):220–228
Chen Q, Yang L, Yang G, Yin Y, Meng X (2017) DFVR: deformable finger vein recognition. In: 2017 IEEE International conference on acoustics, speech and signal processing, ICASSP 2017, New Orleans, LA, USA, 5–9 Mar 2017, pp 1278–1282
Choi JH, Song W, Kim T, Lee S-R, Kim HC (2009) Finger vein extraction using gradient normalization and principal curvature. Proc SPIE 7251:9
Chuang S-J (2018) Vein recognition based on minutiae features in the dorsal venous network of the hand. Signal Image Video Process 12(3):573–581
Connie T, Teoh A, Goh M, Ngo D (2005) PalmHashing: a novel approach for cancelable biometrics. Inf Process Lett 93:1–5
Cortés F, Aranda JM, Sánchez-Reillo R, Meléndez J, López F (2009) Spectral selection for a biometric recognition system based on hand veins detection through image spectrometry. In: BIOSIG 2009—Proceedings of the special interest group on biometrics and electronic signatures, 17–18 Sept 2009 in Darmstadt, Germany, pp 81–92
Costa P, Galdran A, Ines Meyer M, Niemeijer M, Abramoff M, Mendonça AM, Campilho AJC (2018) End-to-end adversarial retinal image synthesis. IEEE Trans Med Imaging 37(3):781–791
Crihalmeanu S, Ross A (2012) Multispectral scleral patterns for ocular biometric recognition. Pattern Recognit Lett 33(14):1860–1869
Cui J, Wang Y, Huang JZ, Tan T, Sun Z (2004) An iris image synthesis method based on PCA and super-resolution. In: Proceedings of the 17th international conference on pattern recognition, 2004. ICPR 2004, vol 4, pp 471–474
Damak W, Trabelsi RB, Masmoudi AD, Sellami D, Nait-Ali A (2016) Age and gender classification from finger vein patterns. In: Intelligent systems design and applications—16th international conference on intelligent systems design and applications (ISDA 2016) held in Porto, Portugal, 16–18 Dec 2016, pp 811–820
Das R, Piciucco E, Maiorana E, Campisi P (2019) Convolutional neural network for finger-vein-based biometric identification. IEEE Trans Inf Forensics Secur 14(2):360–373
Das S, Malathy C (2018) Survey on diagnosis of diseases from retinal images. J Phys: Conf Ser 1000(1):012053
Das A, Mondal P, Pal U, Blumenstein M, Ferrer MA (2016) Sclera vessel pattern synthesis based on a non-parametric texture synthesis technique. In: Raman B, Kumar S, Roy P, Sen D (eds) Proceedings of international conference on computer vision and image processing, volume 460 of Advances in intelligent systems and computing. Springer, pp 241–250
Das A, Pal U, Ballester MAF, Blumenstein M (2014) A new efficient and adaptive sclera recognition system. In: IEEE Symposium on computational intelligence in biometrics and identity management (CIBIM’14), pp 1–8
Das A, Pal U, Blumenstein M, Ballester MAF (2013) Sclera recognition—a survey. In: Second IAPR Asian conference on pattern recognition (ACPR’13), pp 917–921
Das A, Pal U, Ferrer MA, Blumenstein M (2015) SSBC 2015: sclera segmentation benchmarking competition. In: IEEE 7th international conference on biometrics theory, applications and systems, BTAS 2015, Arlington, VA, USA, 8–11 Sept 2015
Das A, Pal U, Ferrer MA, Blumenstein M (2016) SSRBC 2016: sclera segmentation and recognition benchmarking competition. In: International conference on biometrics, ICB 2016, Halmstad, Sweden, 13–16 June 2016
Das A, Pal U, Ferrer MA, Blumenstein M, Stepec D, Rot P, Emersic Z, Peer P, Struc V (2018) SSBC 2018: sclera segmentation benchmarking competition. In: 2018 International conference on biometrics, ICB 2018, Gold Coast, Australia, 20–23 Feb 2018, pp 303–308
Das A, Pal U, Ferrer MA, Blumenstein M, Stepec D, Rot P, Emersic Z, Peer P, Struc V, Aruna Kumar SV, Harish BS (2017) SSERBC 2017: sclera segmentation and eye recognition benchmarking competition. In: 2017 IEEE international joint conference on biometrics, IJCB 2017, Denver, CO, USA, 1–4 Oct 2017, pp 742–747
Das A, Pal U, Ferrer-Ballester MA, Blumenstein M (2014) A new wrist vein biometric system. In: 2014 IEEE symposium on computational intelligence in biometrics and identity management, CIBIM 2014, Orlando, FL, USA, 9–12 Dec 2014, pp 68–75
De Marsico M, Nappi M, Narducci F, Proença H (2018) Insights into the results of MICHE I—Mobile Iris CHallenge Evaluation. Pattern Recognit 74:286–304
De Marsico M, Nappi M, Proença H (2017) Results from MICHE II—Mobile Iris CHallenge Evaluation II. Pattern Recognit Lett 91:3–10
De Marsico M, Nappi M, Riccio D, Wechsler H (2015) Mobile Iris CHallenge Evaluation (MICHE)-I, biometric iris dataset and protocols. Pattern Recognit Lett 57:17–23. Mobile Iris CHallenge Evaluation part I (MICHE I)
Debiasi L, Kauba C, Prommegger B, Uhl A (2018) Near-infrared illumination add-on for mobile hand-vein acquisition. In: 2018 IEEE 9th international conference on biometrics theory, applications and systems (BTAS). Los Angeles, California, USA, pp 1–9
Dehghani A, Ghassabi Z, Moghddam H, Moin M (2013) Human recognition based on retinal images and using new similarity function. EURASIP J Image Video Process 2013:58
Drahansky M, Dolezel M, Urbanek J, Brezinova E, Kim T-H (2012) Influence of skin diseases on fingerprint recognition. J Biomed Biotechnol 2012:Article ID 626148
Elhussieny N, El-Rewaidy H, Fahmy AS (2016) Low cost system for screening cardiovascular diseases in large population: preliminary results. In: 13th International IEEE symposium on biomedical imaging (ISBI’18)
Elnasir S, Mariyam Shamsuddin S, Farokhi S (2015) Accurate palm vein recognition based on wavelet scattering and spectral regression kernel discriminant analysis. J Electron Imaging 24(1):013031
Eng PC, Khalil-Hani M (2009) FPGA-based embedded hand vein biometric authentication system. In: TENCON 2009—2009 IEEE region 10 conference, pp 1–5
Fadhil RI, George LE (2017) Finger vein identification and authentication system. LAP Lambert Academic Publishing, Germany
Fang Y, Qiuxia W, Kang W (2018) A novel finger vein verification system based on two-stream convolutional network learning. Neurocomputing 290:100–107
Fatima J, Syed AM, Akram MU (2013) Feature point validation for improved retina recognition. In: IEEE Workshop on biometric measurements and systems for security and medical applications, 2013, pp 13–16
Faundez-Zanuy M, Mekyska J, Font-Aragonès X (2014) A new hand image database simultaneously acquired in visible, near-infrared and thermal spectrums. Cogn Comput 6(2):230–240
Favre M, Picard S, Bringer J, Chabanne H (2015) Balancing is the key—performing finger vein template protection using fuzzy commitment. In: ICISSP 2015–Proceedings of the 1st international conference on information systems security and privacy, ESEO, Angers, Loire Valley, France, 9–11 Feb 2015, pp 304–311
Fiorini S, De Biasi M, Ballerini L, Trucco E, Ruggeri A (2014) Automatic generation of synthetic retinal fundus images. In: Giachetti A (ed) Smart tools and apps for graphics—Eurographics Italian chapter conference. The Eurographics Association
Fletcher RR, Raghavan V, Zha R, Haverkamp M, Hibberd PL (2014) Development of mobile-based hand vein biometrics for global health patient identification. In: IEEE Global humanitarian technology conference (GHTC 2014), pp 541–547
Frucci M, Riccio D, di Baja GS, Serino L (2018) Using direction and score information for retina based person verification. Expert Syst Appl 94:1–10
Fuhrmann T, Hämmerle-Uhl J, Uhl A (2009) Usefulness of retina codes in biometrics. In: Advances in image and video technology: proceedings of the 3rd Pacific-Rim symposium on image and video technology, PSIVT ’09, volume 5414 of Lecture notes in computer science, Tokyo, Japan, Jan 2009. Springer, pp 624–632
Fuksis R, Greitans M, Nikisins O, Pudzs M (2010) Infrared imaging system for analysis of blood vessel structure. Elektronika IR Elektrotechnika 97(1):45–48
Galbally J, Ross A, Gomez-Barrero M, Fierrez J, Ortega-Garcia J (2013) Iris image reconstruction from binary templates: an efficient probabilistic approach based on genetic algorithms. Comput Vis Image Underst 117(10):1512–1525
Giancardo L, Meriaudeau F, Karnowski TP, Chaum E, Tobin K (2010) Quality assessment of retinal fundus images using elliptical local vessel density. In: New developments in biomedical engineering. IntechOpen
Gomez-Barrero M, Rathgeb C, Li G, Ramachandra R, Galbally J, Busch C (2018) Multi-biometric template protection based on bloom filters. Inf Fusion 42:37–50
Greitans M, Pudzs M, Fuksis R (2010) Palm vein biometrics based on infrared imaging and complex matched filtering. In: Multimedia and security workshop, ACM MM&Sec 2010, Roma, Italy, 9–10 Sept 2010, pp 101–106
Gruschina A (2015) VeinPLUS: a transillumination and reflection-based hand vein database. In: Proceedings of the 39th annual workshop of the Austrian association for pattern recognition (OAGM’15), 2015. arXiv:1505.06769
Guan Y, Wei X, Li Ch-T (2014) On the generalization power of face and gait in gender recognition. Int J Digit Crime Forensics 6(1)
Guibas JT, Virdi TS, Li PS (2017) Synthetic medical images from dual generative adversarial networks. arXiv:1709.01872
Gupta P, Gupta P (2015) An accurate finger vein based verification system. Digit Signal Process 38:43–52
Haddock LJ, Qian C (2015) Smartphone technology for fundus photography. Retin Phys 12(6):51–58
Haiying Liu L, Yang GY, Yin Y (2018) Discriminative binary descriptor for finger vein recognition. IEEE Access 6:5795–5804
Hao Y, Sun Z, Tan T (2007) Comparative studies on multispectral palm image fusion for biometrics. Comput Vis-ACCV 2007:12–21
Harmer K, Howells G (2012) Direct template-free encryption key generation from palm-veins. In: 2012 Third international conference on emerging security technologies, Lisbon, Portugal, 5–7 Sept 2012, pp 70–73
Hartung D (2012) Vascular pattern recognition and its application in privacy–preserving biometric online–banking system. PhD thesis, PhD dissertation, Gjovik University College,
Hartung D, Aastrup Olsen M, Xu H, Thanh Nguyen H, Busch C (2012) Comprehensive analysis of spectral minutiae for vein pattern recognition. IET Biom 1(1):25–36
Hartung D, Busch C (2009) Why vein recognition needs privacy protection. In: Fifth international conference on intelligent information hiding and multimedia signal processing (IIH-MSP’09), pp 1090–1095
Hartung D, Tistarelli M, Busch C (2013) Vein minutia cylinder-codes (V-MCC). In: International conference on biometrics, ICB 2013, June 4–7 2013, Madrid, Spain, pp 1–7
Hatanaka Y, Tajima M, Kawasaki R, Saito K, Ogohara K, Muramatsu C, Sunayama W, Fujita H (2017) Retinal biometrics based on iterative closest point algorithm. In: 2017 39th Annual international conference of the IEEE engineering in medicine and biology society (EMBC), Jeju Island, South Korea, 11–15 July 2017, pp 373–376
Heenaye M, Khan M (2012) A multimodal hand vein biometric based on score level fusion. Procedia Eng 41:897–903. In: International symposium on robotics and intelligent sensors 2012 (IRIS 2012)
Hillerström F, Kumar A, Veldhuis R (2014) Generating and analyzing synthetic finger vein images. In: Proceedings of the international conference of the biometrics special interest group (BIOSIG’14), Sept 2014, pp 121–132
Himaga M, Ogota H (2019) Evolution of finger vein biometric devices in terms of usability. In: Uhl A, Busch C, Marcel S, Veldhuis R (eds) Handbook of vascular biometrics. Springer Science+Business Media, Boston, MA, USA, pp 159–178
Hong HG, Lee MB, Park KR (2017) Convolutional neural network-based finger-vein recognition using NIR image sensors. Sensors 17(6):1297
Hoover A, Kouznetsova V, Goldbaum M (2000) Locating blood vessels in retinal images by piece-wise threhsold probing of a matched filter response. IEEE Trans Med Imaging 19(3):203–210
Huafeng Q, ElYacoubi MA (2017) Deep representation-based feature extraction and recovering for finger-vein verification. IEEE Trans Inf Forensics Secur 12(8):1816–1829
Huang D, Tang Y, Wang Y, Chen L, Wang Y (2015) Hand-dorsa vein recognition by matching local features of multisource keypoints. IEEE Trans Cybern 45(9):1823–1837
Huang D, Zhu X, Wang Y, Zhang D (2016) Dorsal hand vein recognition via hierarchical combination of texture and shape clues. Neurocomputing 214:815–828
Huang B, Dai Y, Li R, Tang D, Li W (2010) Finger-vein authentication based on wide line detector and pattern normalization. In: 2010 20th International conference on pattern recognition (ICPR). IEEE, pp 1269–1272
Huang F, Dashtbozorg B, Tan T, ter Haar Romeny BM (2018) Retinal artery/vein classification using genetic-search feature selection. Comput Methods Programs Biomed 161:197–207
Huang Z, Kang W, Wu Q, Zhao J, Jia W (2016) A finger vein identification system based on image quality assessment. In: Chinese conference on biometric recognition (CCBR’16), volume 9967 of Springer lecture notes in computer science, pp 244–254
Islam R, Abdul Goffar Khan M (2012) Retina recognition: secure biometric authentication system—an approach to implement the eye recognition system using artificial neural networks. LAP Lambert Academic Publishing, Germany
Itqan KS, Radzi S, Gong FG, Mustafa N, Wong YC, Mat ibrahim M (2016) User identification system based on finger-vein patterns using convolutional neural network. ARPN J Eng Appl Sci 11(5):3316–3319
Jain AK, Nandakumar K, Nagar A (2008) Biometric template security. EURASIP J Adv Signal Process 1–17:2008
Jalilian E, Uhl A (2018) Finger-vein recognition using deep fully convolutional neural semantic segmentation networks: the impact of training data. In: Proceedings of the IEEE 10th international workshop on information forensics and security (WIFS 2018), Hong Kong, pp 1–8
Jalilian E, Uhl A (2019) Enhanced segmentation-CNN based finger-vein recognition by joint training with automatically generated and manual labels. In: Proceedings of the IEEE 5th international conference on identity, security and behavior analysis (ISBA 2019), IDRBT, pp 1–8
Jalilian E, Uhl A (2019) Improved CNN-segmentation based finger-vein recognition using automatically generated and fused training labels. In: Uhl A, Busch C, Marcel S, Veldhuis R (eds) Handbook of vascular biometrics. Springer Science+Business Media, Boston, MA, USA, pp 201–224
Jeffers J, Arakala A, Horadam KJ (2010) Entropy of feature point-based retina templates. In: 20th International conference on pattern recognition (ICPR’10), pp 213–216
Jiaqiang W, Ming Y, Hanbing Q, Bin L (2013) Analysis of palm vein image quality and recognition with different distance. In: 2013 Fourth international conference on digital manufacturing automation, pp 215–218
Jini K, Lu H, Sun Z, Cheng C, Ye J, Qian D (2017) Telemedicine screening of retinal diseases with a handheld portable non-mydriatic fundus camera. BMC Ophthalmol 17:89
Juric S, Flis V, Debevc M, Holzinger A, Zalik B (2014) Towards a low-cost mobile subcutaneous vein detection solution using near-infrared spectroscopy. Sci World J, page ID 365902
Kabacinski R, Kowalski M (2011) Vein pattern database and benchmark results. Electron Lett 47(20):1127–1128
Kang W, Qiuxia W (2014) Contactless palm vein recognition using a mutual foreground-based local binary pattern. IEEE Trans Inf Forensics Secur 9(11):1974–1985
Kanhangad V, Kumar A, Zhang D (2011) Contactless and pose invariant biometric identification using hand surface. IEEE Trans Image Process 20(5):1415–1424
Kauba C, Piciucco E, Maiorana E, Campisi P, Uhl A (2016) Advanced variants of feature level fusion for finger vein recognition. In: Proceedings of the international conference of the biometrics special interest group (BIOSIG’16), Darmstadt, Germany, pp 1–12
Kauba C, Prommegger B, Uhl A (2018) Focussing the beam—a new laser illumination based data set providing insights to finger-vein recognition. In: 2018 IEEE 9th International conference on biometrics theory, applications and systems (BTAS). Los Angeles, California, USA, pp 1–9
Kauba C, Prommegger B, Uhl A (2018) The two sides of the finger—an evaluation on the recognition performance of dorsal vs. palmar finger-veins. In: Proceedings of the international conference of the biometrics special interest group (BIOSIG’18), Darmstadt, Germany, pp 1–8
Kauba C, Prommegger B, Uhl A (2019) OpenVein—an open-source modular multi-purpose finger-vein scanner design. In: Uhl A, Busch C, Marcel S, Veldhuis R (eds) Handbook of vascular biometrics. Springer Science+Business Media, Boston, MA, USA, pp 77–112
Kauba C, Reissig J, Uhl A (2014) Pre-processing cascades and fusion in finger vein recognition. In: Proceedings of the international conference of the biometrics special interest group (BIOSIG’14), Darmstadt, Germany
Kauba C, Uhl A (2018) Shedding light on the veins—reflected light or transillumination in hand-vein recognition. In: Proceedings of the 11th IAPR/IEEE international conference on biometrics (ICB’18), Gold Coast, Queensland, Australia, pp 1–8
Kauba C, Uhl A (2019) An available open source vein recognition framework. In: Uhl A, Busch C, Marcel S, Veldhuis R (eds) Handbook of vascular biometrics. Springer Science+Business Media, Boston, MA, USA, pp 113–144
Khalil-Hani M, Eng PC (2010) FPGA-based embedded system implementation of finger vein biometrics. In: IEEE Symposium on industrial electronics and applications (ISIEA’10), pp 700–705
Khalil-Hani M, Eng PC (2011) Personal verification using finger vein biometrics in FPGA-based system-on-chip. In: 7th International conference on electrical and electronics engineering (ELECO’11), pp II-171–II-176
Khanamiri HN, Nakatsuka A, El-Annan J (2017) Smartphone fundus photography. J Vis Exp 125:55958
Kharabe S, Nalini C (2018) Survey on finger-vein segmentation and authentication. Int J Eng Technol 7(1–2):9–14
Kirchgasser S, Kauba C, Uhl A (2019) Cancellable biometrics for finger vein recognition—application in the feature level domain. In: Uhl A, Busch C, Marcel S, Veldhuis R (eds) Handbook of vascular biometrics. Springer Science+Business Media, Boston, MA, USA, pp 481–506
Kirchgasser S, Kauba C, Uhl A (2019) Towards understanding acquisition conditions influencing finger-vein recognition. In: Uhl A, Busch C, Marcel S, Veldhuis R (eds) Handbook of vascular biometrics. Springer Science+Business Media, Boston, MA, USA, pp 179–200
Kocher D, Schwarz S, Uhl A (2016) Empirical evaluation of LBP-extension features for finger vein spoofing detection. In: Proceedings of the international conference of the biometrics special interest group (BIOSIG’16), Darmstadt, Germany, p 8
Köhler T, Budai A, Kraus MF, Odstrcilik J, Michelson G, Hornegger J (2013) Automatic no-reference quality assessment for retinal fundus images using vessel segmentation. In: Proceedings of the 26th IEEE international symposium on computer-based medical systems, pp 95–100
Kolberg J, Gomez-Barrero M, Venkatesh S, Ramachandra R, Busch C (2019) Presentation attack detection for finger recognition. In: Uhl A, Busch C, Marcel S, Veldhuis R (eds) Handbook of vascular biometrics. Springer Science+Business Media, Boston, MA, USA, pp 435–464
Kono M, Ueki H, Umemura S (2002) Near-infrared finger vein patterns for personal identification. Appl Opt 41(35):7429–7436
Köse C, Ikibas C (2011) A personal identification system using retinal vasculature in retinal fundus images. Expert Syst Appl 38(11):13670–13681
Krivokuca V, Gomez-Barrero M, Marcel S, Rathgeb C, Busch C (2019)Towards measuring the amount of discriminatory information in fingervein biometric characteristics using a relative entropy estimator. In: Uhl A, Busch C, Marcel S, Veldhuis R (eds) Handbook of vascular biometrics. Springer Science+Business Media, Boston, MA, USA, pp 507–526
Krivokuca V, Marcel S (2019) On the recognition performance of BioHash-protected fingervein templates. In: Uhl A, Busch C, Marcel S, Veldhuis R (eds) Handbook of vascular biometrics. Springer Science+Business Media, Boston, MA, USA, pp 465–480
Kumar A, Zhou Y (2012) Human identification using finger images. IEEE Trans Image Process 21(4):2228–2244
Kumar A, Venkata Prathyusha K (2009) Personal authentication using hand vein triangulation and knuckle shape. IEEE Trans Image Process 18(9):2127–2136
Kurban OC, Niyaz O, Yildirim T (2016) Neural network based wrist vein identification using ordinary camera. In: International symposium on innovations in Intelligent SysTems and Applications, INISTA 2016, Sinaia, Romania, 2–5 Aug 2016, pp 1–4
Ladoux P-O, Rosenberger C, Dorizzi B (2009) Palm vein verification system based on SIFT matching. In: Advances in biometrics, third international conference, ICB 2009, Alghero, Italy, 2–5 June 2009. Proceedings, pp 1290–1298
Lajevardi SM, Arakala A, Davis SA, Horadam KJ (2013) Retina verification system based on biometric graph matching. IEEE Trans Image Process 22(9):3625–3635
Lalithamani N, Sabrigiriraj M (2015) Dual encryption algorithm to improve security in hand vein and palm vein-based biometric recognition. J Med Imaging Health Inform 5(3):545–551
Lalithamani N, Sabrigiriraj M (2015) Palm and hand vein-based fuzzy vault generation scheme for multibiometric cryptosystem. Imaging Sci J 63(2):111–118
Leandro JJG, Cesar RM, Jelinek HF (2001) Blood vessels segmentation in retina: preliminary assessment of the mathematical morphology and of the wavelet transform techniques. In: Proceedings of Brazilian symposium on computer graphics, image processing and vision, (SIBGRAPI-01), Florianopolis, Brazil, pp 84–90
Lee EC, Jung H, Kim D (2011) New finger biometric method using near infrared imaging. Sensors 11(3):2319–2333
Lee EC, Lee HC, Park KR (2009) Finger vein recognition using minutia-based alignment and local binary pattern-based feature extraction. Int J Imaging Syst Technol 19(3):179–186
Lee HC, Park KR, Kang BJ, Park SJ (2009) A new mobile multimodal biometric device integrating finger vein and fingerprint recognition. In: Proceedings of the 4th international conference on ubiquitous information technologies applications, pp 1–4
Lee J-C (2012) A novel biometric system based on palm vein image. Pattern Recognit Lett 33(12):1520–1528
Lee J, Moon S et al (2017) Imaging of the finger vein and blood flow for anti-spoofing authentication using a laser and a mems scanner. Sensors 17(4):925
Lee J-C, Lo T-M, Chang C-P (2016) Dorsal hand vein recognition based on directional filter bank. Signal Image Video Process 10(1):145–152
Li X, Huang D, Wang Y (2016) Comparative study of deep learning methods on dorsal hand vein recognition. In: Chinese conference on biometric recognition (CCBR’16). Springer, pp 296–306
Lin T, Zheng Y (2003) Node-matching-based pattern recognition method for retinal blood vessel images. Opt Eng 42(11):3302–3306
Lin S-J, Yang C-M, Yeh P-T, Ho T-C (2014) Smartphone fundoscopy for retinopathy of prematurity. Taiwan J Ophthalmol 4(2):82–85
Liu Z, Song S (2012) An embedded real-time finger-vein recognition system for mobile devices. IEEE Trans Consum Electron 58(2):522–527
Liu F, Yang G, Yin Y, Wang S (2014) Singular value decomposition based minutiae matching method for finger vein recognition. Neurocomputing 145:75–89
Liu Y, Ling J, Liu Z, Shen J, Gao C (2018) Finger vein secure biometric template generation based on deep learning. Soft Comput 22(7):2257–2265
Li F, Zhang T, Liu Y, Wang G (2017) Hand-dorsa vein recognition based on scale and contrast invariant feature matching. IEICE Trans 100-D(12):3054–3058
Luo H, Fa-Xin Y, Pan J-S, Chu S-C, Tsai P-W (2010) A survey of vein recognition techniques. Inf Technol J 9(6):1142–1149
Lu Y, Xie SJ, Yoon S, Wang Z, Park DS (2013) An available database for the research of finger vein recognition. In: 2013 6th International congress on image and signal processing (CISP), vol 1. IEEE, pp 410–415
Lu Y, Yoon S, Park DS (2014) Finger vein identification system using two cameras. Electron Lett 50(22):1591–1593
Ma X, Jing X, Huang H, Cui Y, Junsheng M (2017) Palm vein recognition scheme based on an adaptive gabor filter. IET Biom 6(5):325–333
Maamari RN, Keenan JD, Fletcher DA, Margolis T (2014) A mobile phone-based retinal camera for portable wide field imaging. Br J Ophthalmol 98:438–441
Ma H, Cui FP, Oluwatoyin P (2013) A non-contact finger vein image quality assessment method. In: Measurement technology and its application, volume 239 of Applied mechanics and materials. Trans Tech Publications, pp 986–989
Mahri N, Suandi SAS, Rosdi BA (2010) Finger vein recognition algorithm using phase only correlation. In: 2010 International workshop on emerging techniques and challenges for hand-based biometrics (ETCHB). IEEE, pp 1–6
Maier A, Niederbrucker G, Stenger S, Uhl A (2012) Efficient focus assessment for a computer vision-based Vickers hardness measurement system. J Electron Imaging 21:021114
Maio D, Maltoni D, Cappelli R, Wayman JL, Jain AK (2004) FVC2004: third fingerprint verification competition. In: ICBA, volume 3072 of LNCS. Springer, pp 1–7
Maltoni D, Maio D, Jain AK, Prabhakar S (2009) Synthetic fingerprint generation. Handbook of fingerprint recognition, pp 271–302
Mancini DM, Bolinger L, Li H, Kendrick K, Chance B, Wilson JR (1994) Validation of near-infrared spectroscopy in humans. J Appl Physiol 77(6):2740–2747
Marcel S, Nixon MS, Li SZ (eds) (2014) Handbook of biometric anti-spoofing. Springer
Matsuda Y, Miura N, Nagasaka A, Kiyomizu H, Miyatake T (2016) Finger-vein authentication based on deformation-tolerant feature-point matching. Mach Vis Appl 27(2):237–250
Matsuda Y, Miura N, Nonomura Y, Nagasaka A, Miyatake T (2017) Walkthrough-style multi-finger vein authentication. In: Proceedings of the IEEE international conference on consumer electronics (ICCE’17), pp 438–441
Ma H, Wang K, Fan L, Cui F (2013) A finger vein image quality assessment method using object and human visual system index. In: Proceedings of the third Sino-foreign-interchange conference on intelligent science and intelligent data engineering (IScIDE’12), pages 498–506. Springer
Mazumdar JB, Nirmala SR (2018) Retina based biometric authentication system: a review. Int J Adv Res Comput Sci 9(1)
Meenakshi VS, Padmavathi G (2009) Security analysis of hardened retina based fuzzy vault. In: 2009 International conference on advances in recent technologies in communication and computing, pp 926–930
Meng Z, Gu X (2014) Hand vein identification using local gabor ordinal measure. J Electron Imaging 23(5):053004
Meng X, Yin Y, Yang G, Xi X (2013) Retinal identification based on an improved circular gabor filter and scale invariant feature transform. Sensors 13(7):9248–9266
Mesbah R, McCane B, Mills S (2017) Conditional random fields incorporate convolutional neural networks for human eye sclera semantic segmentation. In: 2017 IEEE International joint conference on biometrics, IJCB 2017, Denver, CO, USA, 1–4 Oct 2017, pp 768–773
Miri M, Amini Z, Rabbani H, Kafieh R (2017) A comprehensive study of retinal vessel classification methods in fundus images. J Med Signals Sens 7(2):59–70
Mirmohamadsadeghi L, Drygajlo A (2014) Palm vein recognition with local texture patterns. IET Biom 3(4):198–206
Mirmohamadsadeghi L, Drygajlo A (2011) Palm vein recognition with local binary patterns and local derivative patterns. In: 2011 International joint conference on biometrics (IJCB). IEEE, pp 1–6
Miura N, Nagasaka A, Miyatake T (2004) Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification. Mach Vis Appl 15(4):194–203
Miura N, Nagasaka A, Miyatake T (2007) Extraction of finger-vein patterns using maximum curvature points in image profiles. IEICE Trans Inf Syst 90(8):1185–1194
Modi SK, Elliott SJ, Whetsone J, Kim H (2007) Impact of age groups on fingerprint recognition performance. In: IEEE Workshop on automatic identification advanced technologies, pp 19–23
Mohamed C, Akhtar Z, Boukezzoula N-E, Falk TH (2017) Combining left and right wrist vein images for personal verification. In: Seventh international conference on image processing theory, tools and applications, IPTA 2017, Montreal, QC, Canada, 28 Nov–1 Dec 2017, pp 1–6
Mokroß B-A, Drozdowski P, Rathgeb C, Busch C (2019) Efficient identification in large-scale vein recognition systems using spectral minutiae representations. In: Uhl A, Busch C, Marcel S, Veldhuis R (eds) Handbook of vascular biometrics. Springer Science+Business Media, Boston, MA, USA, pp 225–260
Morales A, Ferrer MA, Kumar A (2011) Towards contactless palmprint authentication. IET Comput Vis 5:407–416
Mo J, Zhang L (2017) Multi-level deep supervised networks for retinal vessel segmentation. Int J Comput Assist Radiol Surg 12(12):2181–2193
Mythily B, Sathyaseelan K (2015) Measuring the quality of image for fake biometric detection: application to finger vein. In: National conference on research advances in communication, computation, electrical science and structures (NCRACCESS), pp 6–11
Nazari P, Pourghassem H (2017) A novel retina-based human identification algorithm based on geometrical shape features using a hierarchical matching structure. Comput Methods Programs Biomed 141:43–58
Nguyen DT, Park YH, Shin KY, Kwon SY, Lee HC, Park KR (2013) Fake finger-vein image detection based on Fourier and wavelet transforms. Digit Signal Process 23(5):1401–1413
Nguyen DT, Park YH, Shin KY, Park KR (2013) New finger-vein recognition method based on image quality assessment. KSII Trans Internet Inf Syst 7(2):347–365
Nguyen DT, Yoon HS, Pham TD, Park KR (2017) Spoof detection for finger-vein recognition system using NIR camera. Sensors 17(10):2261
Nikisins O, Eglitis T, Anjos A, Marcel S (2018) Fast cross-correlation based wrist vein recognition algorithm with rotation and translation compensation. In: International workshop on biometrics and forensics (IWBF’18), pp 1–7
Nikisins O, Eglitis T, Pudzs M, Greitans M (2015) Algorithms for a novel touchless bimodal palm biometric system. In: International conference on biometrics (ICB’15), pp 436–443
Oh K, Oh B-S, Toh K-A, Yau W-Y, Eng H-L (2014) Combining sclera and periocular features for multi-modal identity verification. Neurocomputing 128:185–198
Oh K, Toh K (2012) Extracting sclera features for cancelable identity verification. In: 5th IAPR International conference on biometrics (ICB’12), pp 245–250
Ong EP, Xu Y, Wong DWK, Liu J (2015) Retina verification using a combined points and edges approach. In: 2015 IEEE International conference on image processing, ICIP 2015, Quebec City, QC, Canada, 27–30 Sept 2015, pp 2720–2724
Ortega M, Penedo MG, Rouco J, Barreira N, Carreira MJ (2009) Personal verification based on extraction and characterization of retinal feature points. J Vis Lang Comput 20(2):80–90
Pabitha M, Latha L (2013) Efficient approach for retinal biometric template security and person authentication using noninvertible constructions. Int J Comput Appl 69(4):28–34
Pan M, Kang W (2011) Palm vein recognition based on three local invariant feature extraction algorithms. In: 6th Chinese conference on biometric recognition, CCBR 2011, Beijing, China, 3–4 Dec 2011. Proceedings, pp 116–124
Pascual JES, Uriarte-Antonio J, Sanchez-Reillo R, Lorenz MG (2010) Capturing hand or wrist vein images for biometric authentication using low-cost devices. In: Proceedings of the sixth international conference on intelligent information hiding and multimedia signal processing (IIH-MSP 2010), pp 816–819
Pascual JES, Uriarte-Antonio J, Sánchez-Reillo R, Lorenz MG (2010) Capturing hand or wrist vein images for biometric authentication using low-cost devices. In: Sixth international conference on intelligent information hiding and multimedia signal processing (IIH-MSP 2010), Darmstadt, Germany, 15–17 Oct 2010, Proceedings, pp 318–322
Patil I, Bhilare S, Kanhangad V (2016) Assessing vulnerability of dorsal hand-vein verification system to spoofing attacks using smartphone camera. In: IEEE International conference on identity, security and behavior analysis (ISBA’16), pp 1–6
Paulus J, Meier J, Bock R, Hornegger J, Michelson G (2010) Automated quality assessment of retinal fundus photos. Int J Comput Assist Radiol Surg 5(6):557–564
Peng J, Li Q, Niu X (2014) A novel finger vein image quality evaluation method based on triangular norm. In Tenth international conference on intelligent information hiding and multimedia signal processing (IIHMSP’14), pp 239–242
Piciucco E, Maiorana E, Kauba C, Uhl A, Campisi P (2016) Cancelable biometrics for finger vein recognition. In: Proceedings of the 1st workshop on sensing, processing and learning for intelligent machines (SPLINE 2016), Aalborg, Denmark, pp 1–6
Prasanalakshmi B, Kannammal A (2010) Secure cryptosystem from palm vein biometrics in smart card. In: The 2nd international conference on computer and automation engineering (ICCAE’10), vol 1, pp 653–657
Proenca H, Alexandre LA (2005) UBIRIS: a noisy iris image database. In: Roli F, Vitulano S (eds) Image analysis and processing—ICIAP 2005, vol 3617. Lecture notes on computer science, Sept 2005, Cagliari, Italy, pp 970–977
Proenca H, Filipe S, Santos R, Oliveira J, Alexandre LA (2010) The UBIRIS.v2: a database of visible wavelength images captured on-the-move and at-a-distance. IEEE Trans Pattern Anal Mach Intell 32(8):1529–1535
Prommegger B, Kauba C, Linortner M, Uhl A (2019) Longitudinal finger rotation—deformation detection and correction. IEEE Trans Biom Behav Identity Sci 1(2):123–138
Prommegger B, Kauba C, Uhl A (2018) Longitudinal finger rotation—problems and effects in finger-vein recognition. In: Proceedings of the international conference of the biometrics special interest group (BIOSIG’18), Darmstadt, Germany, pp 1–11
Prommegger B, Kauba C, Uhl A (2018) Multi-perspective finger-vein biometrics. In: 2018 IEEE 9th International conference on biometrics theory, applications and systems (BTAS). Los Angeles, California, USA, pp 1–9
Prommegger B, Kauba C, Uhl A (2019) Different views on the finger—score level fusion in multi-perspective finger vein recognition. In: Uhl A, Busch C, Marcel S, Veldhuis R (eds) Handbook of vascular biometrics. Springer Science+Business Media, Boston, MA, USA, pp 261–308
Qamber S, Waheed Z, Akram MU (2012) Personal identification system based on vascular pattern of human retina. In: Cairo international biomedical engineering conference (CIBEC’12), pp 64–67
Qin H, El-Yacoubi MA (2018) Deep representation for finger-vein image-quality assessment. IEEE Trans Circuits Syst Video Technol 28(8):1677–1693
Qin H, Qin L, Xue L, He X, Chengbo Y, Liang X (2013) Finger-vein verification based on multi-features fusion. Sensors 13(11):15048–15067
Qin H, Chen Z, He X (2018) Finger-vein image quality evaluation based on the representation of grayscale and binary image. Multim Tools Appl 77(2):2505–2527
Qin H, El Yacoubi M (2015) Finger-vein quality assessment by representation learning from binary images. In: International conference on neural information processing (ICONIP’15), volume 9489 of Springer LNCS, pp 421–431
Qin H, Li S, Kot AC, Qin L (2012) Quality assessment of finger-vein image. In: Proceedings of the 2012 Asia Pacific signal and information processing association annual summit and conference
Qiu X, Kang W, Tian S, Jia W, Huang Z (2018) Finger vein presentation attack detection using total variation decomposition. IEEE Trans Inf Forensics Secur 13(2):465–477
Qiu X, Tian S, Kang W, Jia W, Wu Q (2017) Finger vein presentation attack detection using convolutional neural networks. In: Chinese conference on biometric recognition (CCBR’17), volume 10568 of Springer lecture notes in computer science, pp 296–305
Qi Y, Zhou Y, Zhou C, Hu X, Hu X (2016) 3D feature array involved registration algorithm for multi-pose hand vein authentication. In: International conference on biometrics, ICB 2016, Halmstad, Sweden, 13–16 June 2016, pp 1–7
Qi Y, Zhou Y, Zhou C, Hu X, Hu X (2016) Vein point cloud registration algorithm for multi-pose hand vein authentication. In: IEEE International conference on identity, security and behavior analysis, ISBA 2016, Sendai, Japan, 29 Feb–2 Mar 2016, pp 1–6
Radu P, Ferryman JM, Wild P (2015) A robust sclera segmentation algorithm. In: IEEE 7th International conference on biometrics theory, applications and systems, BTAS 2015, Arlington, VA, USA, 8–11 Sept 2015, pp 1–6
Raghavendra R, Avinash M, Marcel S, Busch C (2015) Finger vein liveness detection using motion magnification. In: Proceedings of the seventh IEEE international conference on biometrics: theory, applications and systems (BTAS’15)
Raghavendra R, Busch C (2015) Exploring dorsal finger vein pattern for robust person recognition. In: 2015 International conference on biometrics (ICB), pp 341–348
Raghavendra R, Busch C (2015) Presentation attack detection algorithms for finger vein biometrics: a comprehensive study. In: 11th International conference on signal-image technology internet-based systems (SITIS’15), pp 628–632
Raghavendra R, Busch C (2016) A low cost wrist vein sensor for biometric authentication. In: Proceedings of the 2016 IEEE international conference on imaging systems and techniques (IST)
Raghavendra R, Raja KB, Surbiryala J, Busch C (2014) A low-cost multimodal biometric sensor to capture finger vein and fingerprint. In: 2014 IEEE International joint conference on biometrics (IJCB). IEEE, pp 1–7
Raghavendra R, Raja KB, Venkattesh S, Busch C (2018) Fingervein presentation attack detection using transferable features from deep convolutional networks. In: Deep learning in biometrics, pp 97–104
Raghavendra R, Raja K, Venkatesh S, Busch C (2017) Transferable deep convolutional neural network features for fingervein presentation attack detection. In: Proceedings of the 5th international workshop on biometrics and forensics (IWBF’17), Coventry, United Kingdom, pp 1–6
Raghavendra R, Surbiryala J, Raja K, Busch C (2014) Novel finger vascular pattern imaging device for robust biometric verification. In: Proceedings of the 2014 IEEE conference on imaging systems and techniques (IST 2014)
Rahul RC, Cherian M, Mohan M (2015) Literature survey on contactless palm vein recognition. Int J Comput Sci Trends Technol (IJCST) 3(5)
Rathgeb C, Uhl A (2011) A survey on biometric cryptosystems and cancelable biometrics. EURASIP J Inf Secur 3:2011
Razdi SA, Hani MK, Bakhteri R (2016) Finger-vein biometric identification using convolutional neural network. Turk J Electr Eng Comput Sci 24(3):1863–1878
Rot P, Vitek M, Grm K, Emersic Z, Peer P, Struc V (2019) Deep sclera segmentation and recognition. In: Uhl A, Busch C, Marcel S, Veldhuis R (eds) Handbook of vascular biometrics. Springer Science+Business Media, Boston, MA, USA, pp 395–434
Saha S, Fernando B, Cuadros J, Xiao D, Kanagasingam Y (2018) Automated quality assessment of colour fundus images for diabetic retinopathy screening in telemedicine. J Digit Imaging 31(6):869–878
Sato H (2009) Finger vein verification technology for mobile apparatus. In: Proceedings of the international conference on security and cryptography (SECRYPT’09), pp 37–41
Semerad L, Drahansky M (2015) Biometric entropy of retina. In: 2015 International conference on information and digital technologies, pp 302–304
Sequeira AF, Ferryman J, Chen L, Galdi C, Dugelay J-L, Chiesa V, Uhl A, Prommegger B, Kauba C, Kirchgasser S, Grudzien A, Kowalski M, Szklarski L, Maik P, Gmitrowicz P (2018) Protect multimodal DB: a multimodal biometrics dataset envisaging border control. In: Proceedings of the international conference of the biometrics special interest group (BIOSIG’18), Darmstadt, Germany, pp 1–8
Shaheed K, Liu H, Yang G, Qureshi I, Gou J, Yin Y (2018) A systematic review of finger vein recognition techniques. Information 9:213
Shaydyuk NK, Cleland T (2016) Biometric identification via retina scanning with liveness detection using speckle contrast imaging. In: IEEE International Carnahan conference on security technology, ICCST 2016, Orlando, FL, USA, 24–27 Oct 2016, pp 1–5
Shi Y, Yang J, Yang J (2012) A new algorithm for finger-vein image enhancement and segmentation. Inf Sci Ind Appl 4(22):139–144
Shinzaki T (2019) Use case of palm vein authentication. In: Uhl A, Busch C, Marcel S, Veldhuis R (eds) Handbook of vascular biometrics. Springer Science+Business Media, Boston, MA, USA, pp 145–158
Sierro A, Ferrez P, Roduit P (2015) Contact-less palm/finger vein biometric. In: BIOSIG 2015—Proceedings of the 14th international conference of the biometrics special interest group, 9–11 Sept 2015, Darmstadt, Germany, pp 145–156
Sierro A, Ferrez P, Roduit P (2015) Contact-less palm/finger vein biometrics. In: Proceedings of the international conference of the biometrics special interest group (BIOSIG’15), pp 145–156
Sinthanayothin C, Boyce JF, Cook HL, Williamson TH (1999) Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. Br J Ophthalmol 83:902–910
Soares JVB, Leandro JJG, Cesar-Jr RM, Jelinek HF, Cree MJ (2006) Retinal vessel segmentation using the 2-D gabor wavelet and supervised classification. IEEE Trans Med Imaging 25(9):1214–1222
Söllinger D, Trung P, Uhl A (2018) Non-reference image quality assessment and natural scene statistics to counter biometric sensor spoofing. IET Biom 7(4):314–324
Song JH, Kim C, Yoo Y (2015) Vein visualization using a smart phone with multispectral wiener estimation for point-of-care applications. IEEE J Biomed Health Inform 19(2):773–778
Song W, Kim T, Kim HC, Choi JH, Kong H-J, Lee S-R (2011) A finger-vein verification system using mean curvature. Pattern Recognit Lett 32(11):1541–1547
Staal JJ, Abramoff MD, Niemeijer M, Viergever MA, van Ginneken B (2004) Ridge based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23(4):501–509
Swedish T, Roesch K, Lee IK, Rastogi K, Bernstein S, Raskar R (2015) eyeSelfie: self directed eye alignment using reciprocal eye box imaging. ACM Trans Graph 34(4)
Syazana-Itqan K, Syafeeza AR, Saad NM, Hamid NA, Saad WHBM (2016) A review of finger-vein biometrics identification approaches. Indian J Sci Technol 9(32)
Tagkalakis F, Vlachakis D, Megalooikonomou V, Skodras A (2017) A novel approach to finger vein authentication. In: 14th IEEE International symposium on biomedical imaging, ISBI 2017, Melbourne, Australia, 18–21 Apr 2017, pp 659–662
Tang Y, Huang D, Wang Y (2012) Hand-dorsa vein recognition based on multi-level keypoint detection and local feature matching. In: Proceedings of the 21st international conference on pattern recognition, ICPR 2012, Tsukuba, Japan, 11–15 Nov 2012, pp 2837–2840
Ting E, Ibrahim MZ (2018) A review of finger vein recognition system. J Telecommun Electron Comput Eng 10(1–9):167–171
Tirunagari S, Poh N, Bober M, Windridge D (2015) Windowed DMD as a microtexture descriptor for finger vein counter-spoofing in biometrics. In: IEEE International workshop on information forensics and security (WIFS), Nov 2015, pp 1–6
Tome P, Marcel S (2015) On the vulnerability of palm vein recognition to spoofing attacks. In: The 8th IAPR international conference on biometrics (ICB), May 2015
Tome P, Raghavendra R, Busch C, Tirunagari S, Poh N, Shekar BH, Gragnaniello D, Sansone C, Verdoliva L, Marcel S (2015) The 1st competition on counter measures to finger vein spoofing attacks. In: International conference on biometrics (ICB’15), May 2015, pp 513–518
Tome P, Vanoni M, Marcel S (2014) On the vulnerability of finger vein recognition to spoofing attacks. In: Proceedings of the international conference of the biometrics special interest group (BIOSIG’14), Sept 2014, pp 111–120
Ton BT, Veldhuis RNJ (2013) A high quality finger vascular pattern dataset collected using a custom designed capturing device. In: International conference on biometrics, ICB 2013. IEEE
Uhl A, Wild P (2009) Comparing verification performance of kids and adults for fingerprint, palmprint, hand-geometry and digitprint biometrics. In: Proceedings of the 3rd IEEE international conference on biometrics: theory, application, and systems 2009 (IEEE BTAS’09). IEEE Press, pp 1–6
Veiga D, Pereira C, Ferreira M, Gonçalves L, Monteiro J (2014) Quality evaluation of digital fundus images through combined measures. J Med Imaging 1:014001
Veldhuis R, Spreeuwers L, Ton B, Rozendal S (2019) A high quality finger vein dataset collected using a custom designed capture device. In: Uhl A, Busch C, Marcel S, Veldhuis R (eds) Handbook of vascular biometrics. Springer Science+Business Media, Boston, MA, USA, pp 63–76
Vermeer KA, Vos FM, Lemij HG, Vossepoel AM (2004) A model based method for retinal blood vessel detection. Comput Biol Med 34:209–219
Vostatek P, Claridge E, Uusitalo H, Hauta-Kasari M, Fält P, Lensu L (2017) Performance comparison of publicly available retinal blood vessel segmentation methods. Comput Med Imaging Graph 55:2–12
Waheed Z, Akram MU, Waheed A, Khan MA, Shaukat A, Ishaq M (2016) Person identification using vascular and non-vascular retinal features. Comput Electr Eng 53:359–371
Wan H, Chen L, Song H, Yang J (2017) Dorsal hand vein recognition based on convolutional neural networks. In: 2017 IEEE International conference on bioinformatics and biomedicine, BIBM 2017, Kansas City, MO, USA, 13–16 Nov 2017, pp 1215–1221
Wang J-G, Yau W-Y, Suwandy A (2008) Feature-level fusion of palmprint and palm vein for person identification based on a “junction point” representation. In: Proceedings of the international conference on image processing, ICIP 2008, 12–15 Oct 2008, San Diego, California, USA, pp 253–256
Wang J, Wang G (2017) Quality-specific hand vein recognition system. IEEE Trans Inf Forensics Secur 12(11):2599–2610
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612
Wang J-G, Yau W-Y, Suwandy A, Sung E (2008) Person recognition by fusing palmprint and palm vein images based on “Laplacianpalm” representation. Pattern Recognit 41(5):1514–1527
Wang Y, Zhang K, Shark L-K (2014) Personal identification based on multiple keypoint sets of dorsal hand vein images. IET Biom 3(4):234–245
Wang Y, Xie W, Xiaojie Y, Shark L-K (2015) An automatic physical access control system based on hand vein biometric identification. IEEE Trans Consum Electron 61(3):320–327
Wang Y, Zhang D, Qi Q (2016) Liveness detection for dorsal hand vein recognition. Pers Ubiquit Comput 20(3):447–455
Wang Y, Fan Y, Liao W, Li K, Shark L-K, Varley MR (2012) Hand vein recognition based on multiple keypoints sets. In: 5th IAPR International conference on biometrics, ICB 2012, New Delhi, India, 29 Mar–1 Apr 2012, pp 367–371
Wang L, Leedham G, Cho DSY (2008) Minutiae feature analysis for infrared hand vein pattern biometrics. Pattern Recognit 41(3):920–929
Wang C, Sun X, Dong W, Zhu Z, Zheng S, Zeng X (2017) Quality assessment of palm vein image using natural scene statistics. In: Computer vision—second CCF Chinese conference, CCCV 2017, Tianjin, China, 11–14 Oct 2017, Proceedings, Part II, pp 248–255
Wang J, Wang G, Pan Z (2018) Gender attribute mining with hand-dorsa vein image based on unsupervised sparse feature learning. IEICE Trans 101-D(1):257–260
Wang C, Zeng X, Sun X, Dong W, Zhu Z (2017) Quality assessment on near infrared palm vein image. In: 2017 32nd Youth academic annual conference of Chinese association of automation (YAC), pp 1127–1130
Wilson C (2010) Vein pattern recognition: a privacy-enhancing biometric. CRC Press, Boca Raton, FL, US
Wolterink JM, Leiner T, Isgum I (2018) Blood vessel geometry synthesis using generative adversarial networks. In: 1st Conference on medical imaging with deep learning (MIDL 2018)
Wu KS, Lee J-C, Lo T-M, Chang K-C, Chang C-P (2013) A secure palm vein recognition system. J Syst Softw 86(11):2870–2876
Wu Z, Tian L, Li P, Ting W, Jiang M, Wu C (2016) Generating stable biometric keys for flexible cloud computing authentication using finger vein. Inf Sci 433–434:431–447
Xi X, Yang G, Yin Y, Meng X (2013) Finger vein recognition with personalized feature selection. Sensors 13(9):11243–11259
Xi X, Yang L, Yin Y (2017) Learning discriminative binary codes for finger vein recognition. Pattern Recognit 66:26–33
Xian R, Li W (2014) Performance evaluation of finger-vein verification algorithms in PFVR2014. In: Chinese conference on biometric recognition (CCBR’14), volume 8833 of Springer LNCS, pp 244–251
Xian R, Ni L, Li W (2015) The ICB-2015 competition on finger vein recognition. In: International conference on biometrics, ICB 2015, Phuket, Thailand, 19–22 May 2015, pp 85–89
Xie SJ, Zhou B, Yang JC, Lu Y, Pan Y (2013) Novel hierarchical structure based finger vein image quality assessment. In: Proceedings of the Chinese conference on biometric recognition (CCBR’13), volume 8232 of Springer lecture notes in computer science, pp 266–273
Xie C, Kumar A (2018) Finger vein identification using convolutional neural network and supervised discrete hashing. Pattern Recognit Lett
Xu Z, Guo X, Hu X, Chen X, Wang Z (2006) The identification and recognition based on point for blood vessel of ocular fundus. In: Proceedings of the 1st IAPR international conference on biometrics (ICB’06), number 3832 in Lecture notes on computer science, pp 770–776
Yan X, Kang W, Deng F, Qiuxia W (2015) Palm vein recognition based on multi-sampling and feature-level fusion. Neurocomputing 151:798–807
Yang J, Shi Y (2012) Finger-vein roi localization and vein ridge enhancement. Pattern Recognit Lett 33(12):1569–1579
Yang J, Shi Y (2014) Towards finger-vein image restoration and enhancement for finger-vein recognition. Inf Sci 268:33–52
Yang G, Xi X, Yin Y (2012) Finger vein recognition based on a personalized best bit map. Sensors 12:1738–1757
Yang Y, Yang G, Wang S (2012) Finger vein recognition based on multi-instance. Int J Digit Content Technol Appl 6(11):86–94
Yang L, Yang G, Yin Y, Xiao R (2013) Finger vein image quality evaluation using support vector machines. Opt Eng 52(2):027003
Yang J, Shi Y, Jia G (2017) Finger-vein image matching based on adaptive curve transformation. Pattern Recognit 66:34–43
Yang L, Yang G, Xi X, Meng X, Zhang C, Yin Y (2017) Tri-branch vein structure assisted finger vein recognition. IEEE Access 5:21020–21028
Yang W, Wang S, Jiankun H, Guanglou Z, Chaudhry J, Adi E, Valli C (2018) Securing mobile healthcare data: a smart card based cancelable finger-vein bio-cryptosystem. IEEE Access 06:36939–36947
Yang W, Wang S, Jiankun H, Zheng G, Valli C (2018) A fingerprint and finger-vein based cancelable multi-biometric system. Pattern Recognit 78:242–251
Yang W, Hu J, Wang S (2013) A finger-vein based cancellable bio-cryptosystem. In: 7th International conference network and system security, NSS 2013, Madrid, Spain, 3–4 June 2013. Proceedings, pp 784–790
Yang J, Shi Y, Yang J (2012) Finger-vein image restoration based on a biological optical model. In: New trends and developments in biometrics. InTech
Yang J, Yang J (2009) Multi-channel gabor filter design for finger-vein image enhancement. In: Fifth international conference on image and graphics, 2009. ICIG’09. IEEE, pp 87–91
Yang L, Yang G, Yin Y, Xi X (2017) Finger vein recognition with anatomy structure analysis. IEEE Trans Circuits Syst Video Technol
Yang L, Yang G, Yin Y, Zhou L (2014) A survey of finger vein recognition. In: Chinese conference on biometric recognition (CCBR’14, volume 8833 of Springer LNCS, pp 234–243
Yang L, Yang G, Yin Y, Zhou L (2016) User individuality based cost-sensitive learning: a case study in finger vein recognition. In: International conference on biometrics, ICB 2016, Halmstad, Sweden, 13–16 June 2016, pp 1–8
Yang W, Yu X, Liao Q (2009) Personal authentication using finger vein pattern and finger-dorsa texture fusion. In: Proceedings of the 17th ACM international conference on multimedia. ACM, pp 905–908
Ye Y, Ni L, Zheng H, Liu S, Zhu Y, Zhang D, Xiang W, Li W (2016) FVRC2016: the 2nd finger vein recognition competition. In: International conference on biometrics, ICB 2016, Halmstad, Sweden, 13–16 June 2016
Ye Y, Zheng H, Ni L, Liu S, Li W (2016) A study on the individuality of finger vein based on statistical analysis. In: International conference on biometrics, ICB 2016, Halmstad, Sweden, 13–16 June 2016, pp 1–5
Yin Y, Liu L, Sun X (2011) SDUMLA-HMT: a multimodal biometric database. In: The 6th Chinese conference on biometric recognition (CCBR 2011), volume 7098 of Springer lecture notes on computer science, pp 260–268
Yu H, Agurto C, Barriga S, Nemeth SC, Soliz P, Zamora G (2012) Automated image quality evaluation of retinal fundus photographs in diabetic retinopathy screening. In: 2012 IEEE Southwest symposium on image analysis and interpretation, pp 125–128
Yuksel A, Akarun L, Sankur B (2011) Hand vein biometry based on geometry and appearance methods. IET Comput Vis 5(6):398–406
Zhang C, Liu Z, Liu Y, Su F, Chang J, Zhou Y, Zhao Q (2015) Reflection-type finger vein recognition for mobile applications. J Opt Soc Korea 19(5):467–476
Zhang L, Cheng Z, Shen Y, Wang D (2018) Palmprint and palmvein recognition based on DCNN and A new large-scale contactless palmvein dataset. Symmetry 10(4):78
Zhang R, Huang D, Wang Y (2016) Textured detailed graph model for dorsal hand vein recognition: a holistic approach. In: International conference on biometrics, ICB 2016, Halmstad, Sweden, 13–16 June 2016, pp 1–7
Zhang R, Huang D, Wang Y, Wang Y (2015) Improving feature based dorsal hand vein recognition through random keypoint generation and fine-grained matching. In: International conference on biometrics, ICB 2015, Phuket, Thailand, 19–22 May 2015, pp 326–333
Zhang Y, Huang H, Zhang H, Ni L, Xu W, Ahmed NU, Ahmed MdS, Jin Y, Chen Y, Wen J, Li W (2017) ICFVR 2017: 3rd international competition on finger vein recognition. In: 2017 IEEE International joint conference on biometrics, IJCB 2017, Denver, CO, USA, 1–4 Oct 2017, pp 707–714
Zhang C, Li X, Liu Z, Zhao Q, Xu H, Su F (2013) The CFVD reflection-type finger-vein image database with evaluation baseline. In: Biometric recognition. Springer, pp 282–287
Zhang J, Yang J (2009) Finger-vein image enhancement based on combination of gray-level grouping and circular gabor filter. In: International conference on information engineering and computer science, 2009. ICIECS 2009. IEEE, pp 1–4
Zhang Q, Zhou Y, Wang D, Hu X (2013) Personal authentication using hand vein and knuckle shape point cloud matching. In: IEEE Sixth international conference on biometrics: theory, applications and systems, BTAS 2013, Arlington, VA, USA, 29 Sept–2 Oct 2013, pp 1–6
Zhao J, Tian H, Xu W, Li X (2009) A new approach to hand vein image enhancement. In: Second International Conference on Intelligent Computation Technology and Automation, 2009. ICICTA’09, vol 1. IEEE, pp 499–501
Zheng H, Xu Q, Ye Y, Li W (2017) Effects of meteorological factors on finger vein recognition. In: IEEE International conference on identity, security and behavior analysis, ISBA 2017, New Delhi, India, 22–24 Feb 2017, pp 1–8
Zheng H, Ye Y, Ni L, Liu S, Li W (2016) Which finger is the best for finger vein recognition? In: 8th IEEE International conference on biometrics theory, applications and systems, BTAS 2016, Niagara Falls, NY, USA, 6–9 Sept 2016, pp 1–5
Zhong H, Kanhere SS, Chou CT (2017) VeinDeep: smartphone unlock using vein patterns. In: IEEE International conference on pervasive computing and communications (PerCom’17), pp 2–10
Zhou Y, Kumar A (2011) Human identification using palm-vein images. IEEE Trans Inf Forensics Secur 6(4):1259–1274
Zhou L, Gongping Yang L, Yang YY, Li Y (2015) Finger vein image quality evaluation based on support vector regression. Int J Signal Process Image Process Pattern Recognit 8:211–222
Zhou Z, Du EY, Thomas NL, Delp EJ (2012) A new human identification method: sclera recognition. IEEE Trans Syst Man Cybern Part A 42(3):571–583
Zhou Z, Du EY, Thomas NL, Delp EJ (2013) A comprehensive approach for sclera image quality measure. IJBM 5(2):181–198
Zhou Z, Du EY, Thomas NL (2010) A comprehensive sclera image quality measure. In: 11th International conference on control, automation, robotics vision, pp 638–643
Zhou Y, Kumar A (2010) Contactless palm vein identification using multiple representations. In: Fourth IEEE international conference on biometrics: theory applications and systems, BTAS 2010, Washington, DC, USA, 27–29 Sept 2010, pp 1–6
Zou H, Zhang B, Tao Z, Wang X (2016) A finger vein identification method based on template matching. J Phys Conf Ser 680:012001
Zuo J, Schmid NA, Chen X (2007) On generation and analysis of synthetic iris images. IEEE Trans Inf Forensics Secur 2(1):77–90
Acknowledgements
This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 700259. The work was also funded by the Austrian Research Promotion Agency, FFG KIRAS project AUTFingerATM under grant No. 864785.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Copyright information
© 2020 The Author(s)
About this chapter
Cite this chapter
Uhl, A. (2020). State of the Art in Vascular Biometrics. In: Uhl, A., Busch, C., Marcel, S., Veldhuis, R. (eds) Handbook of Vascular Biometrics. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-030-27731-4_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-27731-4_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27730-7
Online ISBN: 978-3-030-27731-4
eBook Packages: Computer ScienceComputer Science (R0)