A robust multimodal approach for emotion recognition

Emotion recognition is one of the latest challenges in intelligent human/computer communication. Most of previous work on emotion recognition focused on extracting emotions from visual or audio information separately. A novel approach is presented in this paper, including both visual and audio from video clips, to recognize the human emotion. The Facial Animation Parameters (FAPs) compliant facial feature tracking based on GASM (GPU based Active Shape Model) is performed on the video to generate two vector streams which represent the expression feature and the visual speech one. To extract effective speech features, based on geodesic distance estimation, we develop an enhanced Lipschitz embedding to embed high dimensional acoustic features into low dimensional space. Combined with the visual vectors, the audio vector is extracted in terms of low dimensional features. Then, a tripled Hidden Markov Model is introduced to perform the recognition which allows the state asynchrony of the audio and visual observation sequences while preserving their natural correlation over time. The experimental results show that this approach outperforms the conventional approaches for emotion recognition.