An Efficient Approach to Face Recognition Using a Modified Center-Symmetric Local Binary Pattern (MCS-LBP)

In this paper, we present a novel face recognition method called Multi-scale block Center-Symmetric Local Binary Pattern (MCS-LBP). The face recognition process mainly consists of three phase: face representation, feature extraction, and classification. However, the most important phase is extraction, in which unique features of the face image are extracted. The Center-Symmetric Local Binary Pattern (CS-LBP) feature can be viewed as a combination of texture-based features and gradient-based features. However, it has less dimensional area; the bit-wise comparison made between two single pixel values is significantly affected by noise and sensitive to image translation and rotation. To address this problem, we present a modified feature called MCS-LBP. Instead of individual pixels, in the modified CS-LBP, the comparison is performed based on average gray values of sub-regions. Hence, it provides more complete representation than the Local Binary Pattern (LBP) and CS-LBP operator. Experiments demonstrate the proposed method.