Facial Expression Recognition Based on the Belief Theory: Comparison with Different Classifiers

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Abstract

This paper presents a system for classifying facial expressions based on a data fusion process relying on the Belief Theory (BeT). Four expressions are considered: joy, surprise, disgust as well as neutral. The proposed system is able to take into account intrinsic doubt about emotion in the recognition process and to handle the fact that each person has his/her own maximal intensity of displaying a particular facial expression. To demonstrate the suitability of our approach for facial expression classification, we compare it with two other standard approaches: the Bayesian Theory (BaT) and the Hidden Markov Models (HMM). The three classification systems use characteristic distances measuring the deformations of facial skeletons. These skeletons result from a contour segmentation of facial permanent features (mouth, eyes and eyebrows). The performances of the classification systems are tested on the Hammal-Caplier database [1] and it is shown that the BeT classifier outperforms both the BaT and HMM classifiers for the considered application.