Journal
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
Volume 46, Issue 3, Pages 542-567Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ijar.2007.02.003
Keywords
classification; facial expressions; data fusion; transferable belief model; Bayesian model
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A method for the classification of facial expressions from the analysis of facial deformations is presented. This classification process is based on the transferable belief model (TBM) framework. Facial expressions are related to the six universal emotions, namely Joy, Surprise, Disgust, Sadness, Anger, Fear, as well as Neutral. The proposed classifier relies on data coming from a contour segmentation technique, which extracts an expression skeleton of facial features (mouth, eyes and eyebrows) and derives simple distance coefficients from every face image of a video sequence. The characteristic distances are fed to a rule-based decision system that relies on the TBM and data fusion in order to assign a facial expression to every face image. In the proposed work, we first demonstrate the feasibility of facial expression classification with simple data (only five facial distances are considered). We also demonstrate the efficiency of TBM for the purpose of emotion classification. The TBM based classifier was compared with a Bayesian classifier working on the same data. Both classifiers were tested on three different databases. (C) 2007 Elsevier Inc. All rights reserved.
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