Deep Learning Techniques for Hand Vein Biometrics: A Comprehensive Review

TL;DR

This comprehensive review explores recent deep learning advancements in hand vein biometrics, highlighting datasets, evaluation metrics, techniques, challenges, and future research directions for secure, contactless biometric authentication.

Contribution

It provides an extensive overview of deep learning methods applied to hand vein recognition, including datasets, performance metrics, transfer learning, and future challenges, which is novel in consolidating recent advancements.

Findings

Deep learning significantly improves hand vein recognition accuracy.

Public datasets and evaluation metrics are summarized for benchmarking.

Future research directions include addressing challenges and exploring innovative techniques.

Abstract

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.