This paper considers the problem of recognizing faces under varying illuminations. First, we investigate the statistics of the derivative of the irradiance images (log) of human face and find that the distribution is very sparse. Based on this observation, we propose an illumination insensitive similarity measure based on the min operator of the derivatives of two images. Our experiments on the CMU-PIE database have shown that the proposed method improves the performance of a face recognition system when the probes are collected under varying lighting conditions.