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Capturing Facial Actions in Video to Revive Expressions of Humans

KalaiSelvi R, Kavitha P, Shunmuganathan K L

Emotion recognition in video is an interesting and important component in Human Machine Interaction (HMI) system. The recognition of emotional information is a key step toward giving computers the ability to interact more naturally and intelligently with people. Video-based facial expression recognition is a challenging problem in computer vision field. Audio-visual emotion recognition can be carried out with video sequence. The video sequence is a mixture of both audio and video information. This paper dealing only with the video information. The video sequence is segmented in to different frames. From that the target frame is selected and face detection is performed. The Facial Feature points around each facial component capture the detailed face shape information using Active Appearance Model. Action Unit classification represent the specific set of facial muscles. This Action Unit is compared with database AUs which are commonly used to describe the human emotion states. This paper introduces a framework based on Dynamic Bayesian Network (DBN) to represent facial evolvement in different levels. General experiments are performed to demonstrate the feasibility and success of the proposed model.