Facial Expression Recognition Based on Fuzzy Networks

In recent years, there has been significant work in effective recognition of human facial expression. In this paper, we consider a new method for facial expression recognition, based on structural differences. The differences are regulated based on comprehensive laws for every expression. This article uses the Fuzzy Nero algorithm to classify support machines that have close fuzzy separation. With this method, fuzzy classification regulation is based on extracting features, which play a vital role in facial recovery. Our suggested method is based on the fact that, if one feature is close, made in different modes, and takes different modes, the transmission is regulated in all modes. The experimental results are discussed using the Cohn-Kanades and JAFFE databases. Results indicate that the suggested approach is better than previous methods, with better confidence (4% superior) than previous methods.