Facial Expression Recognition Using Gabor Wavelt and Artificial Neural Networks

Abstract

This research presents methods of identifying (Facial Expression Recognition). The objective of this recearch is to present a combact texture oriented method , along with the dimensions reductions,so it would be used in the training of three neural networks: ( Single Layer Neural Networks (SLN), Back Propagation Algorithm (BPA) and Cerebellar Model Articulation Controller (CMAC) ) for identifying facial expressions. The proposed methods are called ( intelligent) methods because they can assimilate the variations in facial emotions and hence proved to be better for untrained facial expressions. Conventional methods have limitations, so facial expressions should follow some constraints. Gabor wavelet is used in different angles to extract possible textures of the facial expression, in order to achieve the expression detection accuracy. Higher dimensions of the extracted texture features are further reduced into a two-dimensional vector by using Fisher’s linear discriminant function in order to increase the accuracy of the proposed methods. Tarining and testing have been done on JAFFE database on certain facial expressions ( angry, disgust, happy, sad, surprise and fear) .The performance comparisons of the proposed algorithms are presented. The resultes obtained are acceptable according to international standards.