Facial expression recognition has been researched much in recent years because of their applications in intelligent communication systems. Many methods have been developed based on extracting Local Binary Pattern (LBP) features associating different classifying techniques in order to get more and more better effects of facial expression recognition. In this work, we propose a novel method for recognizing facial expressions based on Local Binary Pattern features and Support Vector Machine with two effective improvements. First is the preprocessing step and second is the method of dividing face images into nonoverlap square regions for extracting LBP features. The method was experimented on three typical kinds of database: small (213 images), medium (2040 images) and large (5130 images). Experimental results show the effectiveness of our method for obtaining remarkably better recognition rate in comparison with other methods.
['Nhan Thi Cao', 'An Hoa Ton-That', 'Hyung Il Choi']
FACIAL EXPRESSION RECOGNITION BASED ON LOCAL BINARY PATTERN FEATURES AND SUPPORT VECTOR MACHINE