# Deep Learning Techniques for Hand Vein Biometrics: A Comprehensive Review

arXiv: https://arxiv.org/abs/2409.07128 · HTML: https://mlsanity.com/paper/2409.07128/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 242 references that are in the map.
- First posted on arXiv 11 Sep 2024.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation](https://mlsanity.com/paper/1404.0736/) (2014)
- [Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems](https://mlsanity.com/paper/1907.10456/) (2019)
- [Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey](https://mlsanity.com/paper/2403.01255/) (2024)
- [Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization](https://mlsanity.com/paper/2304.14535/) (2023)
- [Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review](https://mlsanity.com/paper/2304.10550/) (2023)
- [Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey](https://mlsanity.com/paper/2408.07583/) (2024)
- [Resource-Efficient Neural Networks for Embedded Systems](https://mlsanity.com/paper/2001.03048/) (2020)
- [AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions](https://mlsanity.com/paper/2308.13592/) (2023)
- [Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directions](https://mlsanity.com/paper/2308.04522/) (2023)
- [Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey](https://mlsanity.com/paper/2407.17877/) (2024)
- [PVSNet: Palm Vein Authentication Siamese Network Trained using Triplet Loss and Adaptive Hard Mining by Learning Enforced Domain Specific Features](https://mlsanity.com/paper/1812.06271/) (2018)
- [Convolutional Neural Network with Convolutional Block Attention Module for Finger Vein Recognition](https://mlsanity.com/paper/2202.06673/) (2022)
- [Towards the Generation of Synthetic Images of Palm Vein Patterns: A Review](https://mlsanity.com/paper/2205.10179/) (2022)
- [N-shot Palm Vein Verification Using Siamese Networks](https://mlsanity.com/paper/2109.12808/) (2021)
- [BPFNet: A Unified Framework for Bimodal Palmprint Alignment and Fusion](https://mlsanity.com/paper/2110.01179/) (2021)
- [A Database of Dorsal Hand Vein Images](https://mlsanity.com/paper/2012.05383/) (2020)
- [PAFedFV: Personalized and Asynchronous Federated Learning for Finger Vein Recognition](https://mlsanity.com/paper/2404.13237/) (2024)

## Built on by (most cited first, 2 of 2)

- [Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection](https://mlsanity.com/paper/2504.13186/) (2025)
- [Multilinear subspace learning for person re-identification based fusion of high order tensor features](https://mlsanity.com/paper/2505.15825/) (2025)

## Abstract (arXiv)

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.