Facial expression recognition using model-based feature extraction and action parameters classification

This paper introduces an automatic facial expression recognition system which consists of two parts: facial feature extraction and facial expression recognition. The system applies the point distribution model and the gray-level model to find the facial features. Then the position variations of certain designated points on the facial feature are described by 10 action parameters (APs). There are two phases in the recognition process: the training phase and the recognition phase. In the training phase, given 90 different expressions, the system classifies the principal components of the APs of all training expressions into six different clusters. In the recognition phase, given a facial image sequence, it identifies the facial expressions by extracting the 10 APs, analyzes the principal components, and finally calculates the AP profile correlation for a higher recognition rate. In the experiments, our system has demonstrated that it can recognize the facial expression effectively. (C) 1997 Academic Press.