Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28340
Title: Sparse representation or collaborative representation : which helps face recognition?
Authors: Zhang, L 
Yang, M
Feng, X
Keywords: Face recognition
Image classification
Image representation
Least squares approximations
Issue Date: 2011
Publisher: IEEE
Source: 2011 IEEE International Conference on Computer Vision (ICCV), 6-13 November 2011, Barcelona, p. 471-478 How to cite?
Abstract: As a recently proposed technique, sparse representation based classification (SRC) has been widely used for face recognition (FR). SRC first codes a testing sample as a sparse linear combination of all the training samples, and then classifies the testing sample by evaluating which class leads to the minimum representation error. While the importance of sparsity is much emphasized in SRC and many related works, the use of collaborative representation (CR) in SRC is ignored by most literature. However, is it really the l1-norm sparsity that improves the FR accuracy? This paper devotes to analyze the working mechanism of SRC, and indicates that it is the CR but not the l1-norm sparsity that makes SRC powerful for face classification. Consequently, we propose a very simple yet much more efficient face classification scheme, namely CR based classification with regularized least square (CRC_RLS). The extensive experiments clearly show that CRC_RLS has very competitive classification results, while it has significantly less complexity than SRC.
URI: http://hdl.handle.net/10397/28340
ISBN: 978-1-4577-1101-5
ISSN: 1550-5499
DOI: 10.1109/ICCV.2011.6126277
Appears in Collections:Conference Paper

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