Belief Theory Applied to Facial Expressions Classification

  • Z. Hammal
  • A. Caplier
  • M. Rombaut
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3687)


A novel and efficient approach to facial expression classification based on the belief theory and data fusion is presented and discussed. The considered expressions correspond to three (joy, surprise, disgust) of the six universal emotions as well as the neutral expression. A robust contour segmentation technique is used to generate an expression skeleton with facial permanent features (mouth, eyes and eyebrows). This skeleton is used to determine the facial features deformations occurring when an expression is present on the face defining a set of characteristic distances. In order to be able to recognize “pure” as well as “mixtures” of facial expressions, a belief-theory based fusion process is proposed. The performances and the limits of the proposed recognition method are highlighted thanks to the analysis of a great number of results on three different test databases: the Hammal-Caplier database, the Cohn-Kanade database and the Cottrel database. Preliminary results demonstrate the interest of the proposed approach, as well as its ability to recognize non separable facial expressions.


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  1. 1.
    Shinza, Y., Saito, Y., Kenmochi, Y., Kotani, K.: Facial Expression Analysis by Integrating Information of Feature-Point Positions and Gray Levels of Facial Images. In: IEEE Proc. ICIP, Vancover Canada (2000)Google Scholar
  2. 2.
    Tian, Y., Kanade, T., Cohn, J.: Recognition Actions Units for Facial Expression Analysis. IEEE Trans. PAMI 23(2), 97–115 (2001)Google Scholar
  3. 3.
    Pantic, M., Rothkrantz, L.J.M.: Expert System for Automatic Analysis of Facial Expressions. ELSIVIER Trans. IVC 8, 881–905 (2000)Google Scholar
  4. 4.
    Eveno, N., Caplier, A., Coulon, P.Y.: Automatic and Accurate Lip Tracking. IEEE Trans. CSVT 14, 706–715 (2004)Google Scholar
  5. 5.
    Hammal, Z., Caplier, A.: Eye and Eyebrow Parametric Models for Automatic Segmentation. In: IEEE Proc. SSIAI, Lake Tahoe Nevada (2004)Google Scholar
  6. 6.
    Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)MATHGoogle Scholar
  7. 7.
    Smets, P.: Data Fusion in the Transferable Belief Model. In: Proc. ISIF, Paris France, pp. 21–33 (2000)Google Scholar
  8. 8.
  9. 9.
    Malciu, M., Preteux, F.: MPEG-4 Compliant Tracking of Facial Features in Video Sequences. In: Proc. EUROIMAGE, ICAV3D, Greece, pp. 108–111 (2001)Google Scholar
  10. 10.
  11. 11.
    Dailey, M., Cottrell, G.W., Reilly, J.: California Facial Expressions, CAFE, unpublished digital images, Computer Science and Engineering Department, UCSD (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Z. Hammal
    • 1
  • A. Caplier
    • 1
  • M. Rombaut
    • 1
  1. 1.Laboratory of Images and SignalsGrenobleFrance

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