Abstract: Person Re-Identification (Re-ID) focuses on identifying the same person among disjoint camera views. This task is highly challenging, especially when there exists only several images per person in the database. Aiming at the problem of insufficient number of person images in person re-identification dataset, a method that generates extra training data from the original dataset is proposed. There are two challenges in this work, one is how to get more training data from the original training set, and the other is how to deal with these newly generated training data. The deep convolutional generative adversarial network is used to generate extra unlabeled person images and label smoothing regularization is used to process these newly generated unlabeled person images. In order to further improve the accuracy of person re-identification, a new unsupervised reranking framework is proposed. This framework neither requires to recalculate a new sorted list for each image pairs nor requires any human interaction or label information. Experiments on the datasets Market-1501, CUHK03, and DukeMTMC-reID verify the effectiveness of the proposed method.