Facial Expression Recognition Using Local Transitional Pattern on Gabor Filtered Facial Images

Automatic facial expression analysis plays a major role in catching emotional state of a human being and can be effectively used in the field of human-computer interaction. Using an effective facial feature is the most critical part for a successful facial expression recognition system. This paper proposes a novel approach in pursuit of recognizing facial expression where facial feature is represented by a hybrid of Gabor wavelet transform of an image and local transitional patterncode. Expression images are classified into prototype expressions via support vector machine with different kernels. Experimental results using Cohn-Kanade expression database is compared with other methods to demonstrate the superiority of the proposed approach which successfully identifies more than 95 of facial expressions correctly.