Illumination-Robust Local Pattern Descriptor for Face Recognition

Abstract

In this paper, we propose a simple descriptor called an extended center-symmetric pattern (ECSP) for illumination-robust face recognition. The ECSP operator encodes the texture information of a local face region by emphasizing diagonal components of a previous center-symmetric local binary pattern (CS-LBP). Here, the diagonal components are emphasized because facial textures along the diagonal direction contain much more information than those of other directions. The facial texture information of the ECSP operator is then used as the input image of an image covariance-based feature extraction algorithm. Performance evaluation of the proposed approach was carried out using various binary pattern operators and recognition algorithms on the extended Yale B database. The experimental results demonstrated that the proposed approach achieved better recognition accuracy than other approaches, and we confirmed that the proposed approach is effective against illumination variation.