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.