Unconstrained face recognition remain a challenging problem due to intra-class variations caused by occlusion, disguise, varying orientations, facial expressions, age variations and illumination in real circumstances...etc. the recognition rate of traditional face recognition algorithms would be very low in this conditions. To address this issue, we propose a non-linear extension to the sparse representation classifier adapted to real-world conditions that can be trained using single training sample. We conduct extensive experiments on AR dataset to verify the efficacy of the proposed method.
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