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Joint sparsity matrix learning for multiclass classification applied to face recognition

[+] Author Affiliations
Minna Qiu

Harbin Institute of Technology, Shenzhen Graduate School, Bio-Computing Research Center, Shenzhen 518000, China

Zhengming Li

Harbin Institute of Technology, Shenzhen Graduate School, Bio-Computing Research Center, Shenzhen 518000, China

Guangdong Polytechnic Normal University, Industrial Training Center, Guangzhou 510000, China

Hongzhi Zhang

Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen 518000, China

Charlene Xie

Harbin Institute of Technology, College of Computer Science, Harbin 150000, China

Jian Zhang

Harbin Institute of Technology, Shenzhen Graduate School, Bio-Computing Research Center, Shenzhen 518000, China

J. Electron. Imaging. 23(3), 033007 (May 19, 2014). doi:10.1117/1.JEI.23.3.033007
History: Received December 11, 2013; Revised April 8, 2014; Accepted April 18, 2014
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Abstract.  Multiclass classification is an important problem in pattern recognition. Various classification methods have been proposed in the past few decades. However, most of these classification methods neglect the errors or the noises that exist in samples. As a result, classification accuracy is badly influenced by the errors or noises. In this paper, we propose a joint sparsity matrix learning method, which exploits l2,1-norm minimization to perform multiclass classification. In order to overcome the influence of the errors or noises, we introduce a sparse matrix to explicitly model the errors or noises and apply an iterative procedure to solve the l2,1-norm regularized problem. We perform experiments on four face databases to verify the effectiveness of the proposed method.

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Citation

Minna Qiu ; Zhengming Li ; Hongzhi Zhang ; Charlene Xie and Jian Zhang
"Joint sparsity matrix learning for multiclass classification applied to face recognition", J. Electron. Imaging. 23(3), 033007 (May 19, 2014). ; http://dx.doi.org/10.1117/1.JEI.23.3.033007


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