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Face Recognition plays a very important role in numerous occasions based on visual security in recent days. The current methods of face recognition are to extract the different features of different faces to distinguish from others, so feature extraction has become a vital step in face recognition. For this reason, this paper presents a new fusion of local difference binary (LDB) and histogram of oriented gradients (HOG) for face recognition. We use LDB descriptor to extract the local pattern features of a face image. At the same time, the edge features of the original image are extracted by using HOG descriptor. The proposed new fusion of features improves the shortcomings of the low accuracy and avoids the problems that the dimension of the general fusion of features is too high. The experimental results on ORL and Yale face database verify the validity of the proposed fusion of features.