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To represent facial features effectively, on the basis of local directional patterns (LDP), a reduced -dimension-local directional pattern (RDLDP) is proposed. Firstly, the constraints of LDP encoding mode is modified to complete the pattern reconstruction, and through the XOR of the LDP code, code of each block is calculated. Then, the encoding image is divided into histograms, and the histograms of all areas are connected to form the final descriptor. Finally, the chi square dissimilarity measure between the eigenvectors is computed, and the k-nearest neighbor classifier is adopted to complete the final face recognition. Three public available standard databases, FERET, extended YALE-B, and ORL are adopted in the experiment. The proposed method can be up to 96.97%, 96.10% and 97.61% respectively in the three data sets.And the effectiveness of the proposed algorithm verified by experimental results. Compared with other advanced methods based on local descriptors, the proposed method is superior in accuracy and error recognition rate.