Finger vein recognition is a promising biometric authentication technique that depends on the unique features of vein patterns in the finger for recognition. The existing finger vein recognition methods are based on minutiae features or binary features such as LBP, LLBP, PBBM etc. or from the entire vein pattern. However, the minutiae-based features cannot accurately represent the structural or anatomical aspects of the vein pattern. These issues with the minutia feature led to increased false matches. Recognition based on binary features have limitations such as increased false matches, sensitivity to the translation and rotation, security and privacy issues etc. A feature representation based on the anatomy of vein patterns can be an alternative solution to improve the recognition performance. In the IJCB 2020 conference, we showed that every finger vein image contains one or more of a kind of 4 special vein patterns which we refereed as Fork, Eye, Bridge, and Arch (FEBA). In this paper, we further enlarge this set to 6 vein patterns (F2EB2A) by identifying two variations in the Fork and Bridge vein patterns. Based on 6 anatomical features of the possible 6 vein patterns in a vein image, we define a $6\times $ 6 feature matrix representation for finger vein images. Since this feature representation is based on the anatomical properties of the local vein patterns, it provides template security. Further we show that, the proposed feature representation is invariant to scaling, translation, and rotation changes. The experimental results using two open datasets and an in-house dataset show that the proposed method has a better recognition performance when compared to the existing approaches with an EER around 0.02% and an average recognition accuracy of 98%.

Finger vein has become an appealing biometric trait due to its intrinsic nature, contactless acquisition and anti-spoofing capability when compared to other dominant biometric traits. The state-of-the-art intrinsic recognition derives vein patterns based on either curvature values, line tracking or deep neural networks. However, these methods extract artifacts such as noise, breaks and texture along with veins due to the problems such as irregular shading, poor contrast and blurriness in NIR images which affect the recognition accuracy. To deal with these issues, we propose a novel acquisition mechanism for vein patterns based on the pulsation of the veins. We propose to capture the pulsations from vein videos to accurately isolate the vein patterns. Besides, the proposed framework has an inherent method of detecting liveness along with recognition of the finger vein. To the best of our knowledge, this is the first work that utilizes the finger vein pulsations for biometric recognition. We acquired a finger vein video dataset, from 320 subjects, to evaluate the proposed method. The experimental results indicate that the proposed approach has a better recognition performance compared to the existing image-based approaches with an EER (%) of 0.8 and a recognition accuracy of 96.35%.

Finger vein identification has become a promising biometric modality due to its anti-spoofing capability, time-invariant nature, privacy and security when compared to other predominant biometric traits. In the wake of the recent epidemics and pandemics, the world has recognized the need for hygienic and contactless identification techniques such as finger vein. Although finger vein biometrics has been around for some time, there doesn't exist any classification scheme for finger vein images similar to the Henry classes for fingerprints. For large scale biometric identification systems, an accurate and consistent classification mechanism can significantly reduce the search space and time for matching. In this paper, we first show that finger vein patterns can be classified into four classes namely, Fork, Eye, Bridge and Arch (FEBA) and then propose an identification scheme based on this classification. To the best of our knowledge, this is the first-ever attempt on classifying finger vein images based on intrinsic anatomical features. We obtained a classification accuracy of 95.88% using convolutional neural network and an average reduction of 86.89% in matching time on a heterogeneous database consisting of 4 different datasets. Cross dataset validation and comparison with existing algorithms have been performed to show the efficacy of the proposed classification and matching mechanism.

Finger vein modality is a relatively new area in biometrics that overcomes the limitations of biometric systems based on external features. Despite the fact that finger veins are invisible to naked eye and latent print doesn’t exist, presentation attack on finger veins is possible if stored samples are stolen or compromised. To counter these attacks, liveness was ascertained using learning based methods. However, these methods are designed to detect only finger vein artefact generated using specific materials. Hardware based liveness detection methods make use of intrinsic characteristics of a live body to differentiate living tissues from artificially created materials resembling it. Thus hardware based liveness detection methods appear to be more robust to a wider class of spoofing attacks. In this paper, we propose a finger vein biometric device with a switchblade model sensor plate to ascertain the presence of a live finger. The blood flow pattern obtained from the sensor is hard to replicate and the presence of a physiological signal inherently implies liveness of the subject. The results after comparing quality of the vein images acquired from the proposed device and images from open databases show that the proposed device produces good quality images. The experimental results demonstrate that the developed prototype device with presentation attack detection (PAD) can successfully avert spoof attacks.