Local binary patterns (LBPs) are effective facial texture feature descriptors in face recognition. However, the performance of original LBP-based face recognition methods rapidly deteriorates in the condition of nonmonotonic illumination variations. In order to overcome this drawback, we propose a LBP-based face recognition approach, namely relative gradient LBPs (RGLBPs), in which the relative gradient is first applied to the original face images to extract illumination invariant features. Then, an LBP describes textural and structural features for face recognition. Finally, the chi-square dissimilarity measure and the nearest neighbor classifier are used for classification. The experimental results validate that the proposed approach is efficient for the illumination problem in face recognition and also robust to expression and age variations.