Illumination invariant face recognition in logarithm Discrete Cosine Transform domain

Face is considered to be one of the biometrics in automatic person identification. For face recognition system to be practical, it should be robust to variations in illumination, pose and expression as humans recognize faces irrespective of all these variations. In this paper, we present an illumination invariant face recognition method in the logarithm discrete cosine transform domain. We use an existing illumination normalization technique in the logarithm DCT domain. The main contribution in this paper is that we apply the principal component analysis (PCA) algorithm for feature extraction in the DCT domain. By this, we skip the inverse DCT step and reduce the computational cost. Experimental results on the Yale B database show that we obtain the same results exactly as applying PCA in the spatial domain with the advantage of the reduced computational cost.