In this paper, a method for face description and recognition is proposed, which extracts the histogram sequence of local Gabor binary patterns (HSLGBP) from the magnitudes of Gabor coefficients. Since Gabor feature is robust to illumination and expression variations and has been successfully used in face recognition area. First, the proposed method decomposes the normalized face image by convolving the face image with multi-scale and multi-orientation Gabor filters to extract their corresponding Gabor magnitude maps (GMMs). Then, the local binary patterns (LBP) operates on each GMM to extract the local neighbor pattern. Finally, the input face image is described by the histogram sequence extracted from all these region patterns. The proposed method is robust to illumination, expression and misalignment by combing the Gabor transform, LBP and spatial histogram. In addition, this face modeling method does not need the training set for statistic learning, thus it avoids the generalizability problem. Moreover, how to combine the statistic method in the stage of classification and propose statistic Fisher weight HSLGBP matching method are discussed. The results compared with the published results on FERET face database of changing illumination, expression and aging verify the validity of the proposed method.