Face Recognition Based on Local Directional Pattern Variance (LDPv)

Face recognition is becoming very popular tools for a successful human commuter interaction system. It seems to be a good compromise between reliability and social acceptance and balances security and privacy well. In this paper, we have presented a new appearance-based feature descriptor, the local directional pattern Variance (LDPv), to represent facial components and analyzed its performance for face. A LDP feature is computed from the relative edge response values in all eight directions at each pixel position, and then, the LDPv descriptor of a facial image is generated from the integral projection of each LDP code weighted by its corresponding variance. The final face representation is then described by the concatenated histogram of LDPv of local regions that encodes both global and local texture information. The recognition performance with FERET datasets demonstrates the robustness of proposed LDPv descriptor for representing appearance of facial image over other existing state of the art approaches.