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Collaborative Representation based Classification for Face Recognition
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BibTeX
@MISC{Zhang_collaborativerepresentation,
author = {Lei Zhang and Meng Yang and Xiangchu Feng and Yi Ma and David Zhang},
title = {Collaborative Representation based Classification for Face Recognition},
year = {}
}
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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 l1norm 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.
Keyphrases
collaborative representation face recognition query sample training sample l2 norm characterization coding coefficient different norm special case different instantiation robust face recognition l1norm sparsity constraint key role extensive experiment minimal coding sparse linear combination various instantiation collaborative representation mechanism facial pixel face recognition accuracy face classification interesting result sparse representation facial feature
