Collaborative Representation based Classification for Face Recognition

TL;DR

This paper analyzes how collaborative representation mechanisms, especially in the context of face recognition, are crucial for classification success, and introduces CRC as a general framework extending SRC.

Contribution

It demonstrates that collaborative representation is more important than sparsity in SRC and introduces CRC as a flexible framework with various norm-based instantiations.

Findings

CRC improves face recognition accuracy

Different norms affect robustness to outliers

Extensive experiments validate CRC's effectiveness

Abstract

By coding a query sample as a sparse linear combination of all training samples and then classifying it by evaluating which class leads to the minimal coding residual, sparse representation based classification (SRC) leads to interesting results for robust face recognition. It is widely believed that the l1- norm sparsity constraint on coding coefficients plays a key role in the success of SRC, while its use of all training samples to collaboratively represent the query sample is rather ignored. In this paper we discuss how SRC works, and show that the collaborative representation mechanism used in SRC is much more crucial to its success of face classification. The SRC is a special case of collaborative representation based classification (CRC), which has various instantiations by applying different norms to the coding residual and coding coefficient. More specifically, the l1 or l2 norm characterization of coding residual is related to the robustness of CRC to outlier facial pixels, while the l1 or l2 norm characterization of coding coefficient is related to the degree of discrimination of facial features. Extensive experiments were conducted to verify the face recognition accuracy and efficiency of CRC with different instantiations.