Study on Sparse Representation based Classification for Biometric Verification

TL;DR

This paper introduces a multimodal biometric verification system combining face and ear recognition using sparse representation classification (SRC), demonstrating improved accuracy and efficiency over existing methods through experimental validation.

Contribution

It presents a novel multimodal verification approach leveraging SRC with small random dictionaries, enhancing performance and reducing computational load.

Findings

SRC-based multimodal verification outperforms state-of-the-art methods

Small random dictionaries improve efficiency without sacrificing accuracy

Sparsity metrics provide insights into biometric verification characteristics

Abstract

In this paper, we propose a multimodal verification system integrating face and ear based on sparse representation based classification (SRC). The face and ear query samples are first encoded separately to derive sparsity-based match scores, and which are then combined with sum-rule fusion for verification. Apart from validating the encouraging performance of SRC-based multimodal verification, this paper also dedicates to provide a clear understanding about the characteristics of SRC-based biometric verification. To this end, two sparsity-based metrics, i.e. spare coding error (SCE) and sparse contribution rate (SCR), are involved, together with face and ear unimodal SRC-based verification. As for the issue that SRC-based biometric verification may suffer from heavy computational burden and verification accuracy degradation with increase of enrolled subjects, we argue that it could be properly resolved by exploiting small random dictionary for sparsity-based score computation, which consists of training samples from a limited number of randomly selected subjects. Experimental results demonstrate the superiority of SRC-based multimodal verification compared to the state-of-the-art multimodal methods like likelihood ratio (LLR), support vector machine (SVM), and the sum-rule fusion methods using cosine similarity, meanwhile the idea of using small random dictionary is feasible in both effectiveness and efficiency.