Noise Robust Illumination Invariant Face Recognition via Contourlet Transform in Logarithm Domain

Abstract

Face recognition under varying lighting conditions is an important topic in many real-life applications. In this paper, we propose a novel algorithm for illumination invariant face recognition. We first convert the face images to the logarithm domain, which makes the dark regions brighter. We then use contourlet transform to generate face images that are approximately invariant to illumination change and use collaborative representation-based classifier (CRC) to classify the unknown faces to one known class. We set the approximation subband and a few highest frequency contourlet coefficient subbands to zero values, and then perform the inverse contourlet transform to generate illumination invariant face images. Experimental results show that our proposed algorithm outperforms two existing methods for the Extended Yale Face Database B for high noise levels. Nevertheless, our new method is not as good as existing methods for low noise levels. In addition, our new method is comparable to existing methods for the CMU-PIE face database.