Facial expression recognition from image sequences using optimized feature selection

A novel method for facial expression recognition from sequences of image frames is described and tested. The expression recognition system is fully automatic, and consists of the following modules: face detection, maximum arousal detection, feature extraction, selection of optimal features, and facial expression recognition. The face detection is based on AdaBoost algorithm and is followed by the extraction of frames with the maximum arousal (intensity) of emotion using the inter-frame mutual information criterion. The selected frames are then processed to generate characteristic features based on the log-Gabor filter method combined with an optimal feature selection process, which uses the MIFS algorithm. The system can automatically recognize six expressions: anger, disgust, fear, happiness, sadness and surprise. The selected features were classified using the Naive Bayesian (NB) classifier. The system was tested using image sequences from the Cohn-Kanade database. The percentage of correct classification was increased from 68.9% for the non-optimized features to 79.5% for the optimized set of features.