Revisiting Competitive Coding Approach for Palmprint Recognition: A Linear Discriminant Analysis Perspective

TL;DR

This paper analyzes the competitive coding approach for palmprint recognition using linear discriminant analysis, identifies its limitations, and proposes an improved method called Class-Specific CompCode with nonlinear mapping, demonstrating enhanced accuracy.

Contribution

It provides the first detailed LDA-based analysis of CompCode, introduces Class-Specific CompCode, and applies nonlinear mapping to improve palmprint recognition accuracy.

Findings

CompCode deviates from Fisher's optimal condition.

Class-Specific CompCode improves recognition accuracy.

Experimental results validate the effectiveness of the proposed method.

Abstract

The competitive Coding approach (CompCode) is one of the most promising methods for palmprint recognition. Due to its high performance and simple formulation, it has been continuously studied for many years. However, although numerous variations of CompCode have been proposed, a detailed analysis of the method is still absent. In this paper, we provide a detailed analysis of CompCode from the perspective of linear discriminant analysis (LDA) for the first time. A non-trivial sufficient condition under which the CompCode is optimal in the sense of Fisher's criterion is presented. Based on our analysis, we examined the statistics of palmprints and concluded that CompCode deviates from the optimal condition. To mitigate the deviation, we propose a new method called Class-Specific CompCode that improves CompCode by excluding non-palm-line areas from matching. A nonlinear mapping of the competitive code is also applied in this method to further enhance accuracy. Experiments on two public databases demonstrate the effectiveness of the proposed method.