Local Line Derivative Pattern for face recognition

In this paper, we propose a novel face descriptor for face recognition, named Local Line Derivative Pattern (LLDP). High-order derivative images in two directions are obtained by convolving original images with Sobel Masks. A revised binary coding function is proposed and three standards on arranging the weights are also proposed. Based on the standards, the weights of a line neighborhood in two directions are arranged. The LLDP labels in two directions are calculated with the proposed binary coding function and weights. The labeled image is divided into blocks where spatial histograms are extracted separately and concatenated into an entire histogram as features for recognition. The experiments on the FERET and Extended Yale B show superior performances of the proposed LLDP compared to other existing methods based on the LBP. The results prove that the LLDP has good robustness against expression, illumination and aging variations.