Person independent facial expression analysis using Gabor features and Genetic Algorithm

Over the last decade, automated analysis of human affective behavior has become an active research area in computer science, psychology, neuroscience, and related fields. This study investigates the application of Gabor filter based features in combination of Genetic Algorithm (GA) and Support Vector Machine (SVM) for dynamic analysis of six basic facial expressions from video sequences. Traditionally, a set of Gabor filters is used for feature extraction from static images of face. However, we employed Sum of Difference (SOD) approach to analysis the dynamics of facial expression from a video sequence. We also used GA to overcome the problem of high dimensional feature vectors and computation cost. A local Gabor filter bank with selected frequencies and orientations is produced by GA. The experimental results show that the proposed method is effective for temporal analysis of affective states. The detection rate of six basic emotions has been reached to 92.97% for Cohn-Kanade (CK
⁺
) database.