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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)

Abstract

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.

Keywords

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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References

  1. 1.
    Ekman, P.: Facial Expression and Emotion. American Psychologist, 384–392 (1993)Google Scholar
  2. 2.
    Ekman, P.: Pictures of Facial Effect. Consulting Psychologist (1976)Google Scholar
  3. 3.
    Ekman, P.: Strong Evidence of Universals in Facial Expressions: A Reply to Russell’s Mistaken Critique. Psychological Bulletin 115(2), 268–287 (1994)CrossRefGoogle Scholar
  4. 4.
    Picard, R.W., Vyzas, E., Healey, J.: Toward Machine Emotional Intelligence: Analysis of Affective Physiological State. IEEE Trans. Pattern Analysis and Machine Intelligence 23(10), 1175–1191 (2001)CrossRefGoogle Scholar
  5. 5.
    Picard, R.W.: Affective Computing. MIT Press, Cambridge (1997)Google Scholar
  6. 6.
    Pantic, M., Rothkrantz, L.J.M.: Automatic Analysis of Facial Expressions: The State of the Art. IEEE Trans. Pattern Analysis and Machine Intelligence 22(12) (2000)Google Scholar
  7. 7.
    Pantic, M., Rothkrantz, L.J.M.: An Expert System for Automatic Analysis of Facial Expressions. Imagine and Vision Computing 18(11), 881–905 (2000)CrossRefGoogle Scholar
  8. 8.
    Stork, D.G., Hart, P.E.: Pattern Classification, 2nd edn. Wiley Interscience, New York (1994)Google Scholar
  9. 9.
    Hong, H., Neven, H., von der Malsburg, C.: Online Facial Expression Recognition Based on Personalized Galleries. In: Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 354–359. IEEE Computer Society, Washington (1998)CrossRefGoogle Scholar

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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