A Facial Expression Classification Algorithm Based on Principle Component Analysis

  • Qingzhang Chen
  • Weiyi Zhang
  • Xiaoying Chen
  • Jianghong Han
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


In this paper, we try to develop an analytical framework for classifying human basic emotions. We try to find out what are the major components of each facial expression, what are the patterns that distinguish them from one another. We applied widely used pattern recognition technique-principle component analysis to characterize the feature point displacements of each basic human facial expression for each individual in the existing database. For faces not existent in the database, so called “novel face” in our experiment, we will first find the face in the database that has most likely neutral face to this individual, and base on an assumption that are widely accepted in cognitive science, we will classify this novel face to the category where the most similar one belongs, and classifying his/her facial expression using the so called “expression model” of the most similar individual. This kind of approach has never be exploited before, then we will examine its robustness in our experiment.


Facial Expression Feature Point Facial Image Motion Vector Principle Component Analysis 
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

  • Qingzhang Chen
    • 1
    • 2
  • Weiyi Zhang
    • 1
  • Xiaoying Chen
    • 1
  • Jianghong Han
    • 2
  1. 1.College of Information EngineeringZhejiang University of TechnologyHangzhouChina
  2. 2.School of Computer ScienceHefei University of TechnologyHefeiChina

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