Enhanced Face Preprocessing and Feature Extraction Methods Robust to Illumination Variation

This paper presents an enhanced facial preprocessing and feature extraction technique for an illumination-roust face recognition system. Overall, the proposed face recognition system consists of a novel preprocessing descriptor, a differential two-dimensional principal component analysis technique, and a fusion module as sequential steps. In particular, the proposed system additionally introduces an enhanced center-symmetric local binary pattern as preprocessing descriptor to achieve performance improvement. To verify the proposed system, performance evaluation was carried out using various binary pattern descriptors and recognition algorithms on the extended Yale B database. As a result, the proposed system showed the best recognition accuracy of 99.03% compared to other approaches, and we confirmed that the proposed approach is effective for consumer applications.