A Novel Approach For Finger Vein Verification Based on Self-Taught Learning

TL;DR

This paper introduces a new finger vein verification method utilizing autoencoders to learn discriminative features, modeling veins with Gaussian distributions, achieving state-of-the-art performance on a benchmark dataset.

Contribution

It presents a novel autoencoder-based feature learning approach for finger vein verification, improving biometric authentication accuracy.

Findings

Achieved state-of-the-art results on SDUMLA-HMT dataset.

Demonstrated effective feature representation for finger vein classification.

Modeling veins with Gaussian distribution enhances verification performance.

Abstract

In this paper, we propose a method for user Finger Vein Authentication (FVA) as a biometric system. Using the discriminative features for classifying theses finger veins is one of the main tips that make difference in related works, Thus we propose to learn a set of representative features, based on autoencoders. We model the user finger vein using a Gaussian distribution. Experimental results show that our algorithm perform like a state-of-the-art on SDUMLA-HMT benchmark.