Log-Gabor Weber Descriptor for Face Recognition

It is well recognized that image representation is the most fundamental task of the face recognition, effective and efficient image feature extraction not only has small intraclass variations and large interclass similarity but also robust to the impact of pose, illumination, expression and occlusion. This paper proposes a new local image descriptor for face recognition, named Log–Gabor Weber descriptor (LGWD). The idea of LGWD is based on the image Log-Gabor wavelet representation and the Weber local binary pattern(WLBP) features. The main motivation of the LGWD is to enhance the multiple scales and orientations Log-Gabor magnitude and phase feature by applying the WLBP coding method. Histograms extracted from the encoded magnitude and phase images are concatenated into one to form the image description finally. The experimental results on the ORL, Yale and UMIST face database verify the representation ability of our proposed descriptor.