Facial Feature Selection Based on SVMs by Regularized Risk Minimization

Authors:
Weihong Li;Weiguo Gong;Liping Yang;Weimin Chen;Xiaohua Gu
Affiliations:
Key Lab of Optoelectronic Technology & Systems of Education Ministry of China;Key Lab of Optoelectronic Technology & Systems of Education Ministry of China;Key Lab of Optoelectronic Technology & Systems of Education Ministry of China;Key Lab of Optoelectronic Technology & Systems of Education Ministry of China;Key Lab of Optoelectronic Technology & Systems of Education Ministry of China
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Year:
2006

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Abstract

In this paper we present a method based on SVMs by regularized risk minimization for the facial feature selection aiming at improving performance of the classifier by (1) using WT + KPCA as filter approach to choose a set of more meaningful representatives to replace the original data for feature selection; (2) using SVM RFE iterative procedure as wrapper approach to obtain the optimum feature subset; (3) using regularized risk minimization as feature selection ranking criterion. Experimental results on FERET face database subsets indicate that the proposed method has a significant improvement in the classification accuracy and speed.