Comparing a Transferable Belief Model Capable of Recognizing Facial Expressions with the Latest Human Data

  • Zakia Hammal
  • Martin Arguin
  • Frédéric Gosselin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4841)

Abstract

Despite significant amount of research on automatic classification of facial expressions, recognizing a facial expression remains a complex task to be achieved by a computer vision system. Our approach is based on a close look at the mechanisms of the human visual system, the best automatic facial expression recognition system yet. The proposed model is made for the classification of the six basic facial expressions plus Neutral on static frames based on the permanent facial features deformations using the Transferable Belief Model. The aim of the proposed work is to understand how the model behaves in the same experimental conditions as the human observer, to compare their results and to identify the missing informations so as to enhance the model performances. To do this we have given our TBM based model the ability to deal with partially occluded stimuli and have compared the behavior of this model with that of humans in a recent experiment, in which human participants had to classify the studied expressions that were randomly sampled using Gaussian apertures. Simulations show first the suitability of the TBM to deal with partially occluded facial parts and its ability to optimize the available information to take the best possible decision. Second they show the similarities of the human and model observers performances. Finally, we reveal important differences between the use of facial information in the human and model observers, which open promising perspectives for future developments of automatic systems.

Keywords

Facial Feature Behavior Facial Expressions Classification Transferable Belief Model Bubbles 

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References

  1. 1.
    Darwin, C.: The expression of the emotions in man and animals. London, Murray (1872)Google Scholar
  2. 2.
    Ekman, P., Friesen, W.V., Ellsworth, P.: Emotion in the human face. Pergamon Press, New York (1972)Google Scholar
  3. 3.
    Izard, C.: The face of emotion. Appleton-Century-Crofts New York (1971)Google Scholar
  4. 4.
    Pantic, M., Rothkrantz, L.: Automatic analysis of facial expressions: The state of the art. IEEE Transactions On Pattern Analysis and Machine Intelligence 22(12), 1424–1445 (2000)CrossRefGoogle Scholar
  5. 5.
    Gao, Y., Leung, M., Hui, S., Tananda, M.: Facial expression recognition from line-based caricatures. IEEE Transaction on System, Man and Cybernetics - PART A: System and Humans 33(3) (2003)Google Scholar
  6. 6.
    Abboud, B., Davoine, F., Dang, M.: Facial expression recognition and synthesis based on appearance model. Signal Processing: Image Communication 19(8), 723–740 (2004)CrossRefGoogle Scholar
  7. 7.
    Cohen, I., Cozman, F., Sebe, N., Cirelo, M., Huang, T.: Learning bayesian network classifiers for facial expression recognition using both labeled and unlabeled data. Proc. IEEE Computer Vision and Pattern Recognition  (2003)Google Scholar
  8. 8.
    Hammal, Z., Couvreur, L., Caplier, A., Rombaut, M.: Facial expressions classification: A new approach based on transferable belief model. International Journal of Approximate Reasoning  (2007), doi: 10.1016/j.ijar.2007.02.003Google Scholar
  9. 9.
    Smith, M., Cottrell, G., Gosselin, F., Schyns, P.: Transmitting and decoding facial expressions of emotions. Psychological Science 16, 184–189 (2005)CrossRefGoogle Scholar
  10. 10.
    Gosselin, F., Schyns, P.: Bubbles: A technique to reveal the use of information in recognition. Vision Research 41, 2261–2271 (2001)CrossRefGoogle Scholar
  11. 11.
    Smets, P.: Data fusion in the transferable belief model. In: Proc. of International Conference on Information Fusion, Paris, France, pp. 21–33 (2000)Google Scholar
  12. 12.
    Smets, P., Kruse, R.: The transferable belief model. Artificial Intelligence 66, 191–234 (1994)MATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Smets, P.: Decision making in the tbm: the necessity of the pignistic transformation. International Journal of Approximate Reasoning 38, 133–147 (2005)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Zakia Hammal
    • 1
  • Martin Arguin
    • 1
  • Frédéric Gosselin
    • 1
  1. 1.Département de psychologie, Université de MontréalCanada

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