# N-shot Palm Vein Verification Using Siamese Networks

arXiv: https://arxiv.org/abs/2109.12808 · HTML: https://mlsanity.com/paper/2109.12808/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 27 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [Occlusion Robust Face Recognition Based on Mask Learning with PairwiseDifferential Siamese Network](https://mlsanity.com/paper/1908.06290/) (2019)
- [Speech Emotion Recognition via Contrastive Loss under Siamese Networks](https://mlsanity.com/paper/1910.11174/) (2019)
- [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)

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

- [Deep Learning Techniques for Hand Vein Biometrics: A Comprehensive Review](https://mlsanity.com/paper/2409.07128/) (2024)
- [Video-Based Palm-Vein Authentication under Challenging Conditions](https://mlsanity.com/paper/2609.02776/) (2026)

## Abstract (arXiv)

The use of deep learning methods to extract vascular biometric patterns from the palm surface has been of interest among researchers in recent years. In many biometric recognition tasks, there is a limit in the number of training samples. This is because of limited vein biometric databases being available for research. This restricts the application of deep learning methods to design algorithms that can effectively identify or authenticate people for vein recognition. This paper proposes an architecture using Siamese neural network structure for few shot palm vein identification. The proposed network uses images from both the palms and consists of two sub-nets that share weights to identify a person. The architecture performance was tested on the HK PolyU multi spectral palm vein database with limited samples. The results suggest that the method is effective since it has 91.9% precision, 91.1% recall, 92.2% specificity, 91.5%, F1-Score, and 90.5% accuracy values.