PH-BRINT: Pooled homomorphic binary rotation invariant and noise tolerant representation for face recognition under illumination variations

Abstract

Face recognition under varying illumination conditions is a challenging problem. We propose a simple and effective multiresolution approach Pooled Homomorphic Binary Rotation Invariant and Noise Tolerant (PHBRINT) for face recognition under varying illumination conditions. First, to reduce the effect of illumination, wavelet transform based homomorphic filter is used. Then Binary Rotation Invariant and Noise Tolerant (BRINT) operators with three different scales are employed to extract multiscale local rotation invariant and illumination insensitive texture features. Finally, the discriminative information from the three scales is pooled using MAX pooling operator and localized gradient information is computed by dividing the pooled image into blocks and calculating the gradient magnitude and direction of each block. The PH-BRINT technique has been tested on a challenging face database Extended Yale B, which was captured under varying illumination conditions. The system using minimum distance classifier with L1-norm achieved an average accuracy of 86.91%, which is comparable with the state-of-the-art best illumination-invariant face recognition techniques.