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Lack of training samples always reduces sharply the accuracies of face recognition. Generating virtual training samples is an effective method to improve the performance of face recognition. In this paper, we propose a new method to obtain virtual samples. The method allows the new virtual training samples to be little far from the original training samples. So they are able to contain proper variations of original face image, which caused by changeable illuminations and facial poses and expressions. The face recognition experiments on Feret and AR face databases show the effectiveness and robustness of the proposed method.
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