Recognition of six basic facial expressions by feature-points tracking using RBF neural network and fuzzy inference system

Face expression recognition is useful for designing new interactive devices offering the possibility of new ways for human to interact with computer systems. In this paper, we develop a facial expression recognition system, based on the facial features extracted from facial characteristic points in frontal image sequences. Selected facial feature points were automatically tracked using a cross-correlation based optical flow, and extracted feature vectors were used to classify expressions, using RBF neural networks and a fuzzy inference system (FIS). Then, recognition results from two classifiers were compared with each other. Success rates were about 91.6% using RBF and 89.1% using FIS classifiers