Abstract
Lack of training samples is the primary cause of low accuracy in the face recognition problem. Especially when only one labeled sample per person is available, many algorithms suffer a slump on performance or even fail to work. In order to increase the accuracy in single training samples recognition problem, virtual samples are generated to enlarge the training set. In this paper, we generate the virtual sample by subtracting the test sample from each training sample and proposed the improved CRC for single training sample face recognition. The experiment results on ORL and FERET face database are provided to validate the effectiveness and robustness of the proposed method.
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