Feature selection based on KPCA, SVM and GSFS for face recognition

Authors:
Weihong Li;Weiguo Gong;Yixiong Liang;Weiming Chen
Affiliations:
Key Lab of Optoelectronic Technology & Systems of Education Ministry of China, Chongqing University, Chongqing, China;Key Lab of Optoelectronic Technology & Systems of Education Ministry of China, Chongqing University, Chongqing, China;Key Lab of Optoelectronic Technology & Systems of Education Ministry of China, Chongqing University, Chongqing, China;Key Lab of Optoelectronic Technology & Systems of Education Ministry of China, Chongqing University, Chongqing, China
Venue:
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Year:
2005

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Abstract

The feature selection is very important for improving classifier's accuracy and reducing classifier's running time. In this paper, a novel feature selection method based on KPCA, SVM and GSFS is proposed for face recognition. The proposed method can be described as follows, first KPCA is used for extracting initial face features, secondly, the extracted features are divided into some single feature sets, and then the single feature sets are trained separately by SVM to obtain the best feature set through GSFS. In this way, the dimensionality of the initial features can be reduced and also the best features can be obtained. Experimental results on ORL, IITL and UMIST face databases indicate the effectiveness of the proposed method.