The Log-Gabor transform, which is suitable for analyzing gradually changing data such as in iris and face images, has been widely used in image processing, pattern recognition, and computer vision. In most cases, only the magnitude or phase information of the Log-Gabor transform is considered. However, the complementary effect taken by combining magnitude and phase information simultaneously for an image-feature extraction problem has not been systematically explored in the existing works. We propose a local image descriptor for face recognition, called Log-Gabor Weber descriptor (LGWD). The novelty of our LGWD is twofold: (1) to fully utilize the information from the magnitude or phase feature of multiscale and orientation Log-Gabor transform, we apply the Weber local binary pattern operator to each transform response. (2) The encoded Log-Gabor magnitude and phase information are fused at the feature level by utilizing kernel canonical correlation analysis strategy, considering that feature level information fusion is effective when the modalities are correlated. Experimental results on the AR, Extended Yale B, and UMIST face databases, compared with those available from recent experiments reported in the literature, show that our descriptor yields a better performance than state-of-the art methods.