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In this paper, novel methods for facial emotion recognition in facial image sequences are presented. Our facial emotional feature detection and extracting based on Active Appearance Models (AAM) with Ekman's Facial Action Coding System (FACS). Our approach to facial emotion recognition lies in the dynamic and probabilistic framework based on Dynamic Bayesian Network (DBN) with Kalman Filter for modeling and understanding the temporal phases of facial expressions in image sequences. By combining AAM and DBN, the proposed method can achieve a higher recognition performance level compare with other facial expression recognition methods. The result on the BioID dataset show a recognition accuracy of more than 90% for facial emotion reasoning using the proposed method.