Face Recognition by Using Elongated Local Binary Patterns with Average Maximum Distance Gradient Magnitude

In this paper, we propose a new face recognition approach based on local binary patterns (LBP). The proposed approach has the following novel contributions. (i) As compared with the conventional LBP, anisotropic structures of the facial images can be captured effectively by the proposed approach using elongated neighborhood distribution, which is called the elongated LBP (ELBP). (ii) A new feature, called Average Maximum Distance Gradient Magnitude (AMDGM), is proposed. AMDGM embeds the gray level difference information between the reference pixel and neighboring pixels in each ELBP pattern. (iii) It is found that the ELBP and AMDGM features are well complement with each other. The proposed method is evaluated by performing facial expression recognition experiments on two databases: ORL and FERET. The proposed method is compared with two widely used face recognition approaches. Furthermore, to test the robustness of the proposed method under the condition that the resolution level of the input images is low, we also conduct additional face recognition experiments on the two databases by reducing the resolution of the input facial images. The experimental results show that the proposed method gives the highest recognition accuracy in both normal environment and low image resolution conditions.