With the reduction of training samples, the performance of traditional face recognition methods drops sharply. We propose an improved fractional order singular value decomposition (IFSVDR) method combined with patch based CRC (PCRC) framework. As the performance can be affected when training samples contain noise, we improve the SVD algorithm by using the fractional order to increase the weight of the main orthogonal basis, and decrease the weight of the relatively small basis to reduce the influence of noise on classification results. Then, we use the PCRC to classify the patches which are reconstructed by the IFSVDR. Compared with the classical sparse representation, the idea of ensemble learning enables the PCRC to deal with the small sample size problem. And the CRC has a lower computation complexity than the SRC. Experiments on the extended Yale B and AR face databases show that the proposed IFSVDR combining with the PCRC has a high recognition rate, even in the case of small sample.