In this paper, a face detection method based on local region sparse coding is proposed. First, every local face regions are extracted as training sample. Next, a discriminative dictionary whose atoms have explicit relations with local regions is learned. Then the appearance of a particular local region is determined based on the response of its sparse coding for each detection window. Finally, face location is obtained using position constraints and detection results of local regions. The innovation of the proposed method lies in combining sparse coding and part based model for face detection. Experimental results in Caltech and BioID database show that the proposed method is suitable for small sample size problem and has good detection results in case of occlusion, rotation, complex expressions.