Face recognition using heteroscedastic weighted kernel discriminant analysis

Authors:
Yixiong Liang;Weiguo Gong;Weihong Li;Yingjun Pan
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

In this paper, we propose a novel heteroscedastic weighted kernel discriminant analysis (HW-KDA) method that extends the linear discriminant analysis (LDA) to deal explicitly with heteroscedasticity and nonlinearity of the face pattern's distribution by integrating the weighted pairwise Chernoff criterion and Kernel trick. The proposed algorithm has been tested, in terms of classification rate performance, on the multiview UMIST face database. Results indicate that the HW-KDA methodology is able to achieve excellent performance with only a very small set of features and outperforms other two popular kernel face recognition methods, the kernel PCA (KPCA) and generalized discriminant analysis (GDA).