A Probabilistic Approach Toward Facial Expression Recognition

抄録

In this paper is proposes Facial Expression Recognition system which consists of several simple probabilistic Gabor Wavelet Neural Networks(GWNN), in which the decision is made on whether the given face has the corresponding facial expression or not. Each probabilistic Gabor Wavelet Neural Network consists of feature extractor and Classifier. The feature extractor considers only 6 facial points to extract separable features, which are able to be obtained in such a way that the evaluated separability criterion is minimized by training process. In the classification process, we proposed and used probabilistic Fuzzy Neural Network Model(FNNM), which follows Bayesian minimum risk classification rule with two flexible loss coefficients. The simplified and integrated approach toward probabilistic facial expression recognition shows us a good performance and adaptation capability, and enables the system to recognize facial expressions efficiently.