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Facial emotion recognition using a combining AAM with DBN

If we want to recognize the emotion via the face-to face interaction, first of all, we need to extract the emotional features from the facial image by using a feature extraction method. The active appearance model (AAM) is a well-known method that can represent a non-rigid object, such as face, facial expression regions as emotional features. And then we need to classify the emotional status reliably, robustly. Bayesian Network is a probability based classifier that can represent the probabilistic relationships between sets of facial features. So, in this paper, our approach to facial feature extraction lies in the proposed feature extraction method based on combining AAM with Facial Action Coding System (FACS) for automatically modeling and extracting the facial emotional features. To recognize the facial expression, we use the Dynamic Bayesian Networks (DBNs) for modeling and understanding the temporal phases of facial expressions in image sequences.