An Effective Approach for Facial Expression Recognition with Local Binary Pattern and Support Vector Machine

Many methods have been developed based on extracting Local Binary Pattern 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 non-overlap square regions for extracting Local Binary Pattern features. The method has experimented on three typical kinds of database: small (213 images), medium (1386 images) and large (5130 images). Experimental results show the effectiveness of our method for obtaining remarkably better recognition rate in comparison with other methods. Besides, the proposed approach based on Local Binary Pattern features and Support Vector Machine is simple, fast and significant for real time applications.