Facial expression recognition using two-class discriminant features

Authors:
Marios Kyperountas;Ioannis Pitas
Affiliations:
Department of Informatics, Aristotle University of Thessaloniki, Greece;Department of Informatics, Aristotle University of Thessaloniki, Greece and Informatics and Telematics Institude, CERTH, Greece
Venue:
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
Year:
2009

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Abstract

This paper presents a novel facial expression recognition methodology. In order to classify the expression of a test face to one of seven predetermined facial expression classes, multiple two-class classification tasks are carried out. For each such task, a unique set of features is identified that is enhanced, in terms of its ability to help produce a proper separation between the two specific classes. The selection of these sets of features is accomplished by making use of a class separability measure that is utilized in an iterative process. Fisher's linear discriminant is employed in order to produce the separation between each pair of classes and train each two-class classifier. In order to combine the classification results from all two-class classifiers, the 'voting' classifier-decision fusion process is employed. The standard JAFFE database is utilized in order to evaluate the performance of this algorithm. Experimental results show that the proposed methodology provides a good solution to the facial expression recognition problem.