A single scale retinex based method for palm vein extraction

TL;DR

This paper introduces a single scale Retinex-based method for extracting palm vein images that improves visualization and robustness under asymmetric illumination and shadows, enhancing biometric identification accuracy.

Contribution

It presents a novel application of the single scale Retinex algorithm specifically for palm vein extraction under challenging lighting conditions.

Findings

Contrast ratio increased by 18.4%

Entropy increased by 1.07%

Definition increased by 18.8%

Abstract

Palm vein recognition is a novel biometric identification technology. But how to gain a better vein extraction result from the raw palm image is still a challenging problem, especially when the raw data collection has the problem of asymmetric illumination. This paper proposes a method based on single scale Retinex algorithm to extract palm vein image when strong shadow presents due to asymmetric illumination and uneven geometry of the palm. We test our method on a multispectral palm image. The experimental result shows that the proposed method is robust to the influence of illumination angle and shadow. Compared to the traditional extraction methods, the proposed method can obtain palm vein lines with better visualization performance (the contrast ratio increases by 18.4%, entropy increases by 1.07%, and definition increases by 18.8%).