To overcome the problem of occlusion in face recognition, the method of extended sparse representation for face recognition based on Gabor Features and Metaface Learning (GMFL) is proposed. Considering the robustness of the Gabor feature to the variation of illumination, expressions and gestures, the method extracts Gabor features of images firstly, and then a new dictionary with stronger sparse representation power can be obtained from the Gabor feature sets by Metaface scheme. Meanwhile, the Gabor occlusion dictionary is employed to encode the occluded part of the image, and a set of over-complete dictionary bases are produced. Finally, the test image can be reconstructed by the over-complete dictionary bases, and the residual between the sample and the reconstructed sample is used for classification by minimizing residual. Experimental results demonstrate that the algorithm proposed is valid and robust on AR database and FERET database.