Kernel modified quadratic discriminant function for facial expression recognition

Authors:
Duan-Duan Yang;Lian-Wen Jin;Jun-Xun Yin;Li-Xin Zhen;Jian-Cheng Huang
Affiliations:
Department of Electronic and Communication Engineering, South China University of Technology, Guangzhou, China;Department of Electronic and Communication Engineering, South China University of Technology, Guangzhou, China;Department of Electronic and Communication Engineering, South China University of Technology, Guangzhou, China;Motorola China Research Center, Shanghai, P.R. China;Motorola China Research Center, Shanghai, P.R. China
Venue:
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
Year:
2006

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Abstract

The Modified Quadratic Discriminant Function was first proposed by Kimura et al to improve the performance of Quadratic Discriminant Function, which can be seen as a dot-product method by eigen-decompostion of the covariance matrix of each class. Therefore, it is possible to expand MQDF to high dimension space by kernel trick. This paper presents a new kernel-based method to pattern recognition, Kernel Modified Quadratic Discriminant Function(KMQDF), based on MQDF and kernel method. The proposed KMQDF is applied in facial expression recognition. JAFFE face database and the AR face database are used to test this algorithm. Experimental results show that the proposed KMQDF with appropriated parameters can outperform 1-NN, QDF, MQDF classifier.