Improved face recognition under varying illumination conditions using webface-based local descriptors

An improved face recognition-based method subjected to varying lighting is proposed. It is performed through achieving the following steps. First, Contrast Equalization (CE) is integrated into the framework of Weberface (WF) method. It reduces as much the lighting impact on the input face image as possible, so it becomes more robust than the one ensured by the standard WF method. Second, The separate implementation of the two distinct local descriptors, which are Binarized Statistical Image Features (BSIF) and Local Phase Quantization (LPQ), as well as the fusion of them descriptors allows enhancing the classification accuracy. They are chosen due to their ability to discriminate and their robustness against a variety of facial variations. The proposed methodology is assessed on two public face datasets. It is found that the identification accuracy of these two extended Yale B and AR datasets is quite challenging as compared to all existing methodologies. The simulation results demonstrate the superiority of the proposed method over existing ones in terms of efficiency and improvement ratio.