Uniform Local Derivative Patterns and Their Application in Face Recognition

In recent years, local feature descriptors have received more and more attention due to their effectiveness in the field of face recognition. Local Derivative Patterns (LDPs) for local feature descriptions attract researchers' great interest. However, an LDP produces 2 (p) different patterns for p neighbors through the transition of LDPs for an image, which lead to high dimension features for image analysis. In this paper, LDPs are expanded to Uniform Local Derivative Patterns (ULDPs) that have the same binary encoding way as LDPs but different transition patterns by introducing uniform patterns. A uniform pattern is the one that contains at most two bitwise transitions from 0 to 1 or vice versa when the binary bit is circular. Then, the number of the transition patterns is reduced from 2 (p) to p(p-1)+3 for p neighbors, e.g., 256 to 59 for p = 8. For face recognition, the histogram features are combined together in four directions, and both non-preprocessed and preprocessed images are used to evaluate the performance of the proposed ULDPs method. Extensive experimental results on three publicly available face databases show that the proposed ULDPs approach has better recognition performance than that obtained by using the LDPs method.