Robust Face Recognition Using The Modified Census Transform

Many algorithms do not work well in real-world systems as real-world systems have problems with illumination variation and imperfect detection of face and eyes. In this paper, we compare the illumination normalization methods (SQI, HE, GIC), and the feature extraction methods (PCA, LDA, 2dPCA, 2dLDA, B2dLDA) using Yale B database and ETRJ database. In addition, we propose a stable and robust illumination normalization method using a modified census transform. The experimental results show that MCT is robust for illumination variations as well as for inaccurate eyes and face detections. B2dLDA was shown to have the best performance in the feature extraction methods.