Abstract Person re-identification (Re-ID) is an important task in video surveillance which automatically searches and identifies people across different cameras. Despite the extensive Re-ID progress in RGB cameras, few works have studied the Re-ID between infrared and RGB images, which is essentially a cross-modality problem and widely encountered in real-world scenarios. The key challenge lies in two folds, i.e., the lack of discriminative information to re-identify the same person between RGB and infrared modalities, and the difficulty to learn a robust metric for such a large-scale crossmodality retrieval. In this paper, we tackle the above two challenges by proposing a novel crossmodality generative adversarial network (termed cmGAN). To handle the lack of insufficient discriminative information, we design a cutting-edge generative adversarial training based discriminator to learn discriminative feature representation from different modalities. To handle the issue of largescale cross-modality metric learning, we integrate both identification loss and cross-modality triplet loss, which minimize inter-class ambiguity while maximizing cross-modality similarity among instances. The entire cmGAN can be trained in an end-to-end manner by using standard deep neural network framework. We have quantized the performance of our work in the newly-released SYSU RGB-IR Re-ID benchmark, and have reported superior performance, i.e., Cumulative Match Characteristic curve (CMC) and Mean Average Precision (MAP), over the state-of-the-art works [Wu et al., 2017], at least 12.17% and 11.85% respectively.