A Training Model for Fuzzy Classification System

In recent years, Fuzzy Logic is considerably used as an intelligent technique to solve the ambiguous problems which are in the human life. Classification of emotions is one of challenging problems which is deals with natural conditions of human face, linguistic and paralanguage. As the understanding of emotions, is highly depended on the facial expressions, in this paper a feature based hybrid system is proposed to classify the facial expressions to the basic emotions. The core of expression recognition system is a Mamdani-type fuzzy rule based system to model mathematically the natural conditions. Also, with the purpose of making better performance of fuzzy rule based system, Genetic learning Processes designed for parameter optimization to improve the accuracy and robustness of the system under adverse conditions. To evaluate the system performance, images from Cohn-Kanade database were used and the accuracy rate of 92 % was obtained