A Local Descriptor with Physiological Characteristic for Finger Vein Recognition

TL;DR

This paper introduces a finger vein-specific local feature descriptor called HOPGR, which leverages physiological characteristics for improved recognition accuracy and robustness against local image variations.

Contribution

The paper proposes a novel finger vein-specific descriptor using physiological Gabor responses, incorporating prior directional information for enhanced recognition performance.

Findings

Outperforms most current state-of-the-art methods

Demonstrates robustness against local image changes

Effective in various finger vein databases

Abstract

Local feature descriptors exhibit great superiority in finger vein recognition due to their stability and robustness against local changes in images. However, most of these are methods use general-purpose descriptors that do not consider finger vein-specific features. In this work, we propose a finger vein-specific local feature descriptors based physiological characteristic of finger vein patterns, i.e., histogram of oriented physiological Gabor responses (HOPGR), for finger vein recognition. First, prior of directional characteristic of finger vein patterns is obtained in an unsupervised manner. Then the physiological Gabor filter banks are set up based on the prior information to extract the physiological responses and orientation. Finally, to make feature has robustness against local changes in images, histogram is generated as output by dividing the image into non-overlapping cells and overlapping blocks. Extensive experimental results on several databases clearly demonstrate that the proposed method outperforms most current state-of-the-art finger vein recognition methods.