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This paper addresses the problem of identifying faces when the training face database consists of one face image of each person. It proposes a new approach that synthesizes new face samples of varying degrees of edge information; the synthesized images are generated from the original image and form non-linear approximations of the latter. The approximation is framed as an l 1 minimization problem in a transform domain. The paper also shows that a voting based approach to recognize faces from single available samples yields better results than previous works that only augmented the available database. The proposed approach yields considerably better results (about 6% increase in recognition accuracy) than the SPCA method, which was tailored for addressing this problem.