Person-independent facial expression recognition based on compound local binary pattern (CLBP)

Automatic recognition of facial expression is an active research topic in computer vision due to its importance in both human-computer and social interaction. One of the critical issues for a successful facial expression recognition system is to design a robust facial feature descriptor. Among the different existing methods, the Local Binary Pattern (LBP) has been proved to be a simple and effective one for facial expression representation. However, the LBP method thresholds P neighbors exactly at the value of the center pixel in a local neighborhood and encodes only the signs of the differences between the gray values. Thus, it loses some important texture information. In this paper, we present a robust facial feature descriptor constructed with the Compound Local Binary Pattern (CLBP) for person-independent facial expression recognition, which overcomes the limitations of LBP. The proposed CLBP operator combines extra P bits with the original LBP code in order to construct a robust feature descriptor that exploits both the sign and the magnitude information of the differences between the center and the neighbor gray values. The recognition performance of the proposed method is evaluated using the CohnKanade (CK) and the Japanese Female Facial Expression (JAFFE) database with a Support Vector Machine (SVM) classifier. Experimental results with prototypic expressions show the superiority of the CLBP feature descriptor against some wellknown appearance-based feature representation methods.