N-shot Palm Vein Verification Using Siamese Networks

TL;DR

This paper introduces a Siamese neural network architecture for palm vein verification that performs well even with limited training samples, addressing the challenge of small biometric datasets.

Contribution

It presents a novel few-shot learning approach using Siamese networks for palm vein identification, improving accuracy with limited data.

Findings

Achieved 90.5% accuracy on HK PolyU palm vein database.

Demonstrated high precision and recall, over 91%.

Effective in small-sample scenarios for biometric recognition.

Abstract

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.