At present, face recognition algorithms often ignore the influence of noise in the training process, especially when the training data and the testing data are corrupted, so the recognition performance may be decreased significantly. To solve the problem of face recognition with illumination variation, occlusion, camouflage and expression variation, we propose a new face recognition algorithm based on lowrank subspace projection and Gabor feature via sparse representation. Firstly, we obtain the potential low rank structure and sparse error structure of training samples by the lowrank matrix recovery algorithm, and the transformation matrix of the Gabor feature of the low rank structure by using the principal component analysis.  Then the Gabor feature vectors of training samples and testing samples are projected to the low rank subspace through the transformation matrix. Finally, we use sparse representation classification algorithm to perform classification and recognition in the low rank subspace. Experiments on the Extend Yale B and AR databases show that the proposed method has a high recognition rate and strong robustness.