Model Based Face Recognition Across Facial Expressions

This paper describes a novel idea of face recognition across facial expression variations using model based approach. The approach follows in 1) modeling an active appearance model (AAM) for the face image, 2) using optical flow based temporal features for facial expression variations estimation, 3) and finally applying binary decision trees as a classifier for facial identification. The novelty lies not only in generation of appearance models which is obtained by fitting active shape model (ASM) to the face image using objective but also using a feature vector which is the combination of shape, texture and temporal parameters that is robust against facial expression variations. Experiments have been performed on Cohn-Kanade facial expression database using 61 subjects of the database with image sequences consisting of more than 4000 images. This achieved successful recognition rate up to 91.17% using decision tree as classifier in the presence of six different facial expressions.