Facial Expression recognition using Local Binary Patterns and Kullback Leibler divergence

Facial Expressions play major role in interpersonal communication and imparting intelligence to computer for identifying facial expressions is a crucial task. In this paper we present an efficient preprocessing algorithm combined with feature extraction using Local Binary Patterns (LBP) followed by classification using Kullback Leibler (KL) divergence. Firstly Viola Jones algorithm is used to detect pair of eyes using which effective part of face is obtained which is further processed to eliminate illumination effect. LBP operator is then applied on the preprocessed image to extract local features represented by histogram. Template histograms for seven basic expressions using training images are formed which are compared with the test histogram distribution using an efficient KL divergence for dissimilarity measure. This algorithm is implemented on JAFFE database resulting in a high classification accuracy of 95.24%.