Hand Vein Biometrics Research Articles

Deep learning techniques for hand vein biometrics: A comprehensive review

Biometric authentication has garnered significant attention as a secure and efficient method of identity verification. Among the various modalities, hand vein biometrics, including finger vein, palm vein, and dorsal hand vein recognition, offer unique advantages due to their high accuracy, low susceptibility to forgery, and non-intrusiveness. The vein patterns within the hand are highly complex and distinct for each individual, making them an ideal biometric identifier. Additionally, hand vein recognition is contactless, enhancing user convenience and hygiene compared to other modalities such as fingerprint or iris recognition. Furthermore, the veins are internally located, rendering them less susceptible to damage or alteration, thus enhancing the security and reliability of the biometric system. The combination of these factors makes hand vein biometrics a highly effective and secure method for identity verification.This review paper delves into the latest advancements in deep learning techniques applied to finger vein, palm vein, and dorsal hand vein recognition. It encompasses all essential fundamentals of hand vein biometrics, summarizes publicly available datasets, and discusses state-of-the-art metrics used for evaluating the three modes. Moreover, it provides a comprehensive overview of suggested approaches for finger, palm, dorsal, and multimodal vein techniques, offering insights into the best performance achieved, data augmentation techniques, and effective transfer learning methods, along with associated pretrained deep learning models. Additionally, the review addresses research challenges faced and outlines future directions and perspectives, encouraging researchers to enhance existing methods and propose innovative techniques.

Dorsal hand vein biometrics with a novel deep learning approach for person identification

Hand dorsal biometric recognition system proposed in this study combines the strength of information in regions of dorsal vein biometric trait in a deep learning based Convolutional Neural Networks (CNN) model. The approach divides each dorsal image into five overlapping regions; consequently, five different training and test sets are obtained for each image, modeling a multi-modal biometric system while using only one trait. The test outputs are combined by score-level fusion. Experimental results on FYO, Bosphorus and Badawi datasets indicate the efficiency of the proposed method and its comparability with other recognition systems. The results are also compared with the state-of-the-art dorsal hand vein recognition systems to show the ability of the proposed biometric architecture to perform well in different conditions that may affect dorsal vein pattern acquisition and have con-sequent effect on the efficiency of the recognition system.

Attack Detection for Finger and Palm Vein Biometrics by Fusion of Multiple Recognition Algorithms

Vascular patterns in the hand region are not visible to the naked eye or consumer cameras, therefore finger and hand vein biometrics is often considered invulnerable to presentation attacks. However, one can never rule out the possibility that a malicious attacker manages to create functional attack samples. Various approaches on how to detect such attacks have been proposed, along with publicly available attack databases and varying ideas to create artificial attack samples. In a first step it is important to verify that created presentation attack artifacts hold the potential to deceive a real system. In order to provide a meaningful and comparable threat potential evaluation, this article evaluates 15 existing vein recognition schemes using attack samples derived from three finger vein attack databases and one palm vein attack database. As a second step, in this work we investigate an approach to combine these employed vein recognition schemes and utilizing them to perform presentation attack detection, which to the authors’ best knowledge has not been described in literature so far. Experimental results show that this approach can effectively be used to detect vein attack samples.

A survey on dorsal hand vein biometrics

Biometrics technology is one of the most important and effective solutions for personal authentication. In recent years, as one of the emerging biometrics technologies, dorsal hand vein (DHV) biometrics has received a lot of attention. In fact, DHV biometrics has been studied for more than 30 years, during which different problems related to DHV recognition have been addressed. In this paper, we conduct a comprehensive survey on the state-of-the-art in DHV biometrics. Nearly all important aspects of DHV biometrics have been summarized, including the developmental history, data acquisition, databases, preprocessing algorithms, feature extraction and matching algorithms, information fusion schemes and commercial products. We also discuss the challenges and future directions in DHV biometrics research.

Certain investigation on feature extraction in dorsal hand vein image

Biometrics refers to people being known by their fingerprints or attributes. This research takes into account veins on the back of the hand called the dorsal vein. The primary target of this work is to build up an individual acknowledgment framework dependent available dorsal vein design with high acknowledgment rate. Attributes such as LBP ( Local Binary Pattern), HOG (Histogram of Oriented Gradients) and WLD (Weber Local Descriptor) are extracted and the technique of Chisquare, Cityblock, Euclidean and Minikowski is used for recognition speeds.The quality assessment is conducted in terms of accuracy of category. Hand vein biometrics offer higher security, simple to get the hand vein picture and extremely difficult to fashion the information contrasted with progressively settled biometric check techniques.

An Iterative Deep Neural Network for Hand-Vein Verification

Hand-vein biometrics as a high-security pattern has received more and more attention. One of the open issues in hand-vein verification is the lack of robustness against image quality degradation, which may comprise the verification accuracy. To achieve robust verification, vein feature extraction approaches, especially vein texture segmentation, have been extensively investigated. In recent years, deep neural networks have achieved promising results in medical image segmentation and have been brought into vein verification, but current solutions suffer from two challenges for vein segmentation: 1) lacking the labeling data, which is expensive to obtain and 2) the incorrect label data obtained by manual labeling scheme or automatic labeling scheme may strongly influence parameters when the network is trained, which may degrade the verification performance. This paper proposes an iterative deep belief network (DBN) to extract vein features based on the initial label data, which are automatically generated using a very limited a priori knowledge and iteratively corrected by our DBN. First, a known handcrafted vein image segmentation technique is employed to automatically label vein pixel and background pixel. A training dataset is constructed based on the patches centered on the labeled pixels. Second, a DBN is trained on the resulting database to predict the probability of each pixel to belong to be a vein pixel given a patch centered on it. The vein patterns are segmented using a probability threshold of 0.5. The resulting vein features are employed to reconstruct the training dataset, based on which the network is retrained. During the iterative procedure, the incorrect labels of training data are statistically corrected, which enables DBN to effectively learn what a finger-vein pattern is by learning the difference between vein patterns and background ones. The experimental results on two public hand-vein databases show a significant improvement in terms of hand-vein verification accuracy.

MULTISPECTRAL DORSAL HAND VEIN RECOGNITION BASED ON LOCAL LINE BINARY PATTERN

Nowadays, dorsal hand vein recognition is one of the most recent multispectral biometrics technologies used for the person identification/authentication. Looking into another biometrics system, dorsal hand vein biometrics system has been popular because of the privilege: false duplicity, hygienic, static, and convenient. The most challenging phase in a biometric system is feature extraction phase. In this research, feature extraction method called Local Line Binary Pattern (LLBP) has been explored and implemented. We have used this method to our 300 dorsal hand vein images obtained from 50 persons using a low-cost infrared webcam. In recognition step, the adaptation fuzzy k-NN classifier is to evaluate the efficiency of the proposed approach is feasible and effective for dorsal hand vein recognition. The experimental result showed that LLBP method is reliable for feature extraction on dorsal hand vein recognition with a recognition accuracy up to 98%.

Analysing Factors Affecting Hand Biometrics during Image Capture

As more people are connected digitally, a highly automatic personal identification system is crucial. Dorsal hand vein biometric is an emerging biometric characteristic which is explored at its full swing. Although, researchers have deployed many hand biometrics using interesting techniques, it has not yet been accepted in many applications. Images capture is an important phase where the images obtained determine the performance of the biometric security system. Environmental factors and behavior of the subjects have an effect on image capture. In these work, different variables, that is, distance between camera and hand, the angle of deviation and the environmental temperature are controlled to capture images. The results are analysed and the effect of the variables have been depicted. It is deduced that image capture phase in biometric applications deserve more attention.

Impact of Changing Parameters when Preprocessing Dorsal Hand Vein Pattern

Dorsal hand vein biometric has been the driving force for many researchers lately. The latters have adopted several approaches for preprocessing the vein pattern, extracting its features and matching. Preprocessing steps play an important role in a biometric security system since it allows obtaining the features required for later stages.In line with this, the preprocessing steps of the dorsal hand vein pattern are being scrutinized in a view of procuringideal vein features. Recently more attention is being given to processing and matching stages of a biometric security system. It is therefore mandatory to investigate on the preprocessing factors that affect the performance of a biometric system. Thus, in this work, different preprocessing techniques are being investigated.A database of 500 images is considered for which same number of images and instances are used. Through experimentation, it is found that different techniques give different results which have an impact on the later stages. The result proves that a well-defined extracted vein pattern gives better performance and leads to a more secure biometric authentication system.

A Multimodal Hand Vein Biometric based on Score Level Fusion

Traditional security methods have largely been overtaken by biometrics. Vein pattern characteristics have become the forefront of biometric research because of its uniqueness, stability and immunity to frauds. Researchers are pioneering methods of processing and matching vein patterns. For the last few years, hand vein unimodal biometric has been explored. However, to address the challenges such as intra-class variations, unacceptable error rates and noisy data posed by the latter, multibiometrics has to be developed. Motivated by the fact that multibiometrics improves the accuracy of biometric system, a hand vein biometric comprising of dorsal and palmar vein has been implemented in this work. First, individual scores are generated by the individual matchers and are used for testing the biometric system. These scores are then fused using score level fusion, which is easy to access and combine the scores obtained from the different modalities. Unimodal biometric performance has been compared with hand multimodal biometrics.