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Traditional face recognition algorithms usually handle variations in illumination and pose independently. Therefore, it is difficult to obtain the global optimal recognition performance. To this end, we propose an affine minimum linear reconstruction error(AMLRE) algorithm based on the non-rigid characteristics of human faces in this paper, which combines an affine transformation model and the idea of patch with a linear reconstruction model. Our algorithm simultaneously handles illumination variations as well as compensates the local area alignment errors caused by pose variations, which achieves much better recognition performance. Comprehensive experiments on several public face datasets clearly demonstrate that our proposed algorithm is robust to both illumination and pose, and thus outperforms most state-of-the-art methods.