From Noise to Feature: Exploiting Intensity Distribution as a Novel Soft Biometric Trait for Finger Vein Recognition

TL;DR

This paper introduces a novel approach to finger vein recognition by utilizing the intensity distribution of finger tissue as a soft biometric trait, enhancing recognition accuracy through new extraction algorithms and a hybrid matching strategy.

Contribution

It proposes new algorithms for extracting intensity distribution features from finger vein images and a hybrid matching strategy to improve recognition performance.

Findings

Effective recognition performance demonstrated on three open-access databases.

Intensity distribution features provide complementary information to texture-based features.

The hybrid matching strategy addresses dimension differences successfully.

Abstract

Most finger vein feature extraction algorithms achieve satisfactory performance due to their texture representation abilities, despite simultaneously ignoring the intensity distribution that is formed by the finger tissue, and in some cases, processing it as background noise. In this paper, we exploit this kind of noise as a novel soft biometric trait for achieving better finger vein recognition performance. First, a detailed analysis of the finger vein imaging principle and the characteristics of the image are presented to show that the intensity distribution that is formed by the finger tissue in the background can be extracted as a soft biometric trait for recognition. Then, two finger vein background layer extraction algorithms and three soft biometric trait extraction algorithms are proposed for intensity distribution feature extraction. Finally, a hybrid matching strategy is proposed to solve the issue of dimension difference between the primary and soft biometric traits on the score level. A series of rigorous contrast experiments on three open-access databases demonstrates that our proposed method is feasible and effective for finger vein recognition.