Support vector machine for face emotion detection on real time basis

Enabling computer systems to recognize facial expressions and infer emotions from them in real time presents a challenging research topic. In this paper, a real-time method is proposed as a solution to the problem of facial expression classification in video sequences. We employ an automatic facial feature tracker to perform face localization and feature extraction. The facial feature displacements in the video stream are used as input to a Support Vector Machine classifier. We evaluate our method in terms of recognition accuracy for a variety of interaction and classification scenarios. Our person-dependent and person-independent experiments demonstrate the effectiveness of a support vector machine and feature tracking approach to fully automatic, unobtrusive expression recognition in live video.