To apply supervised learning method in single face recognition problem, an improved algorithm based on sample augments by sliding window is proposed. The recognition time of the proposed algorithm is shorter than that of the original algorithm because of less feature dimension. Moreover, the mirror samples are generated to constitute auxiliary training set and two subspaces can be obtained by subspace learning. The recognition result is from the decision fusion of two subspaces and is robust to variation of the test samples. The experiment results on ORL face database and subset of Feret face database show that the proposed algorithm has higher recognition accuracy than other similar algorithms.