Face Recognition Based on Local Derivative Ternary Pattern

Texture is one of the fundamental features for describing image characteristics. Face can be seen as a composition of micro-patterns of textures. The texture analysis community has proposed a variety of descriptors for face recognition. Local binary pattern (LBP) is a very popular texture operator used in a wide variety of applications including face recognition. Many variants of LBP have been proposed so far and let more emerges due to its overwhelming success. In this series, a new face recognition algorithm called local derivative ternary pattern (LDTP) is proposed in this paper in order to alleviate the face recognition rate under real-time challenges. The strength of the descriptor is demonstrated on four different databases containing more than 2000 face images under variations in lighting, facial expression, and pose. The experimental results show that the proposed LDTP approach provides a better representation of face patterns and achieves higher recognition rates than LBP and its derivatives.