We propose a novel object descriptor, the high order Local Derivative Pattern (LDP), for robust face recognition. In general, LBP can be conceptually considered as a non directional first-order local pattern, which is the binary result of the first-order derivative in images. The second-order LDP can capture the change of derivative directions among local neighbors, and encode the turning point in a given direction. Compared to LBP, the high-order LDP achieved superior performance. Moreover, we propose to extend LDP to feature images. LDP features are directly extracted from gray-level images or feature images without any training procedure. Like LBP, LDP is a micro pattern representation which can also be modeled by histogram to preserve the information about the distribution of the LDP micro patterns. From that LDP feature we extract the histogram values. This histogram values will be used to mapping the dataset feature. Finally it recognize from the database.