Pattern Recognition and Image Analysis

Volume 3687 of the series Lecture Notes in Computer Science pp 183-191

Belief Theory Applied to Facial Expressions Classification

  • Z. HammalAffiliated withLaboratory of Images and Signals
  • , A. CaplierAffiliated withLaboratory of Images and Signals
  • , M. RombautAffiliated withLaboratory of Images and Signals

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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.