GA-Driven LDA in KPCA Space for Facial Expression Recognition

  • Qijun Zhao
  • Hongtao Lu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3611)


Automatic facial expression recognition has been studied comprehensively recently, but most existent algorithms for this task perform not well in presence of nonlinear information in facial images. For this sake, we employ KPCA to map the original facial data to a lower dimensional space. Then LDA is applied in that space and we derive the most discriminant vectors using GA. This method has no singularity problem, which often arises in the traditional eigen decomposition-based solutions to LDA. Other work of this paper includes proposing a rather simple but effective preprocessing method and using Mahalanobis distance rather than Euclidean distance as the metric of the nearest neighbor classifier. Experiments on the JAFFE database show promising results.


Facial Expression Linear Discriminant Analysis Facial Expression Recognition Kernel Principal Component Analysis Facial Action Code System 
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 2005

Authors and Affiliations

  • Qijun Zhao
    • 1
  • Hongtao Lu
    • 1
  1. 1.Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiP.R. China

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