Investigation on Different Pre-processing Approaches for Face Recognition System

One of the challenges in face recognition system is to deal with inhomogeneous intensity problem that occur with different lighting conditions. In this paper, comparisons are made on several pre-processing methods i.e. histogram equalization, local binary pattern, wavelet transform and multiscale retinex. First, the input image is pre-processed with the illumination correction method before the classification task is done. The results are evaluated using the Yale, ORL and our own UKM database. These databases include images with various illumination conditions and expressions. Using PCA as the feature extraction and Euclidean Distance as the classification purposed, our experiments shows that multiscale retinex achieved the lowest equal error rates with 5.03% followed by local binary pattern (7.52%), wavelet transform (12.53%) and histogram equalization (12.97%) on average for all three databases.