The main task of person re-identification is to use computer vision to match and retrieve specific person across view fields. Compared with the traditional algorithm, deep learning is a more appropriate representative method for the discrimination between persons using data-driven extraction features. This study summarized the background and research history, main challenges, main methods, datasets, and evaluation index of person re-identification. The algorithms of person re-identification were mainly analyzed based on three aspects: feature expression, local features, and generative adversarial networks. The accuracy of 9 common datasets, 3 evaluation criteria, and 14 typical methods of person re-identification on the Market1501 dataset was listed. Finally, the prospects for the future research direction of person re-identification were established.