Facial expression recognition using game theory and particle swarm optimization

Authors:
Kaushik Roy;Mohamed S. Kamel
Affiliations:
Centre for Pattern Analysis and Machine Intelligence, University of Waterloo, ON, Canada;Centre for Pattern Analysis and Machine Intelligence, University of Waterloo, ON, Canada
Venue:
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Year:
2012

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Abstract

Robust lip contour detection plays an important role in Facial Expression Recognition (FER). However, the large variations emerged from different speakers, intensity conditions, poor texture of lips, weak contrast between lip and skin, high deformability of lip, beard, moustache, wrinkle, etc. often hamper the lip contour detection accuracy. The novelty of this research effort is that we propose a new lip boundary localization scheme using Game Theory (GT) to elicit lip contour accurately from a facial image. Furthermore, we apply a feature subset selection scheme based on Particle Swarm Optimization (PSO) to select the optimal facial features. We have conducted several sets of experiments to evaluate the proposed approach. The results show that the proposed approach has achieved recognition rates of 93.0% and 92.3% on the JAFFE and CK+ datasets, respectively.