Facial expression recognition using game theory and particle swarm optimization

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. © 2012 Springer-Verlag Berlin Heidelberg.