In this paper we propose an integration of face identification and facial expression recognition. A face is modeled as a graph where the nodes represent facial feature points. This model is used for automatic face and facial feature point detection, and facial feature points tracked by applying flexible feature matching. Face identification is performed by comparing the graphs representing the input face image with individual face models. Facial expression is modeled by finding the relationship between the motion of facial feature points and expression change. Individual and average expression models are generated and then used to identify facial expressions under appropriate categories and the degree of expression changes. The expression model used for facial expression recognition is chosen by the results of face identification.