  Person re-identification is a basic subject in the field of computer vision.
The traditional methods have several limitations in solving the problems of
person illumination like occlusion, pose variation and feature variation under
complex background. Fortunately, deep learning paradigm opens new ways of the
person re-identification research and becomes a hot spot in this field.
Generative Adversarial Nets (GANs) in the past few years attracted lots of
attention in solving these problems. This paper reviews the GAN based methods
for person re-identification focuses on the related papers about different GAN
based frameworks and discusses their advantages and disadvantages. Finally, it
proposes the direction of future research, especially the prospect of person
re-identification methods based on GANs.
