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Uncorrelated regularized local Fisher discriminant analysis for face recognition

[+] Author Affiliations
Zhan Wang

Beijing Jiaotong University, Institute of Information Science, Beijing 100044, China

Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China

Qiuqi Ruan

Beijing Jiaotong University, Institute of Information Science, Beijing 100044, China

Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China

Gaoyun An

Beijing Jiaotong University, Institute of Information Science, Beijing 100044, China

Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China

J. Electron. Imaging. 23(4), 043017 (Aug 04, 2014). doi:10.1117/1.JEI.23.4.043017
History: Received March 28, 2014; Revised June 22, 2014; Accepted June 27, 2014
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Abstract.  A local Fisher discriminant analysis can work well for a multimodal problem. However, it often suffers from the undersampled problem, which makes the local within-class scatter matrix singular. We develop a supervised discriminant analysis technique called uncorrelated regularized local Fisher discriminant analysis for image feature extraction. In this technique, the local within-class scatter matrix is approximated by a full-rank matrix that not only solves the undersampled problem but also eliminates the poor impact of small and zero eigenvalues. Statistically uncorrelated features are obtained to remove redundancy. A trace ratio criterion and the corresponding iterative algorithm are employed to globally solve the objective function. Experimental results on four famous face databases indicate that our proposed method is effective and outperforms the conventional dimensionality reduction methods.

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Topics

Databases ; Matrices

Citation

Zhan Wang ; Qiuqi Ruan and Gaoyun An
"Uncorrelated regularized local Fisher discriminant analysis for face recognition", J. Electron. Imaging. 23(4), 043017 (Aug 04, 2014). ; http://dx.doi.org/10.1117/1.JEI.23.4.043017


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