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Kernel Modified Quadratic Discriminant Function for Facial Expression Recognition

  • Duan-Duan Yang
  • Lian-Wen Jin
  • Jun-Xun Yin
  • Li-Xin Zhen
  • Jian-Cheng Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4153)

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.

Keywords

Linear Discriminant Analysis Principle Component Analysis Machine Intelligence Face Database Facial Expression Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Duan-Duan Yang
    • 1
  • Lian-Wen Jin
    • 1
  • Jun-Xun Yin
    • 1
  • Li-Xin Zhen
    • 2
  • Jian-Cheng Huang
    • 2
  1. 1.Department of Electronic and Communication EngineeringSouth China University of TechnologyGuangzhouChina
  2. 2.Motorola China Research CenterShanghaiP.R. China

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