  RGB-Infrared (IR) person re-identification is an important and challenging
task due to large cross-modality variations between RGB and IR images. Most
conventional approaches aim to bridge the cross-modality gap with feature
alignment by feature representation learning. Different from existing methods,
in this paper, we propose a novel and end-to-end Alignment Generative
Adversarial Network (AlignGAN) for the RGB-IR RE-ID task. The proposed model
enjoys several merits. First, it can exploit pixel alignment and feature
alignment jointly. To the best of our knowledge, this is the first work to
model the two alignment strategies jointly for the RGB-IR RE-ID problem.
Second, the proposed model consists of a pixel generator, a feature generator,
and a joint discriminator. By playing a min-max game among the three
components, our model is able to not only alleviate the cross-modality and
intra-modality variations but also learn identity-consistent features.
Extensive experimental results on two standard benchmarks demonstrate that the
proposed model performs favorably against state-of-the-art methods. Especially,
on SYSU-MM01 dataset, our model can achieve an absolute gain of 15.4% and 12.9%
in terms of Rank-1 and mAP.
