Local Patterns of Gradients for Face Recognition

Local Patterns of Gradients for Face Recognition We present a novel feature extraction method named local patterns of gradients (LPOGs) for robust face recognition. LPOG uses block-wised elliptical local binary patterns (BELBP), a refined variant of ELBP, and local phase quantization (LPQ) operators directly on gradient images for capturing local texture patterns to build up a feature vector of a face image. From one input image, two directional gradient images are computed. A symmetric pair of BELBP and a LPQ operator are then separately applied upon each gradient image to generate local patterns images. Histogram sequences of local patterns images’ nonoverlapped subregions are finally concatenated to form the LPOG vector for the given image. Based on LPOG descriptor, we propose a novel face recognition system which exploits whitened principal component analysis (WPCA) for dimension reduction and weighted angle-based distance for classification. Experimental results on three large public databases (FERET, AR, and SCface) prove that LPOG WPCA system is robust against a wide range of challenges, such as illumination, expression, occlusion, pose, time-lapse variations, and low resolution. In addition, comparison with other systems shows that LPOG WPCA significantly outperforms the state-of-the-art methods. Computationally, timing benchmarks also demonstrate that our LPOG method is faster than many advanced feature extraction algorithms and can be applied in real-world applications.