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Facial Expression Recognition Using HLAC Features and WPCA

  • Fang Liu
  • Zhi-liang Wang
  • Li Wang
  • Xiu-yan Meng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3784)

Abstract

This paper proposes a new facial expression recognition method which combines Higher Order Local Autocorrelation (HLAC) features with Weighted PCA. HLAC features are computed at each pixel in the human face image. Then these features are integrated with a weight map to obtain a feature vector. We select the weight by combining statistic method with psychology theory. The experiments on the “CMU-PITTSBURGH AU-Coded Face Expression Image Database” show that our Weighted PCA method can improve the recognition rate significantly without increasing the computation, when compared with PCA.

Keywords

Facial Expression Recognition Rate Facial Expression Recognition Principal Component Analysis Method Face 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

  • Fang Liu
    • 1
  • Zhi-liang Wang
    • 1
  • Li Wang
    • 1
  • Xiu-yan Meng
    • 1
  1. 1.School of Information EngineeringUniversity of Science & Technology BeijingChina

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