  Person re-identification (re-ID) aims at matching images of the same identity
across camera views. Due to varying distances between cameras and persons of
interest, resolution mismatch can be expected, which would degrade person re-ID
performance in real-world scenarios. To overcome this problem, we propose a
novel generative adversarial network to address cross-resolution person re-ID,
allowing query images with varying resolutions. By advancing adversarial
learning techniques, our proposed model learns resolution-invariant image
representations while being able to recover the missing details in
low-resolution input images. The resulting features can be jointly applied for
improving person re-ID performance due to preserving resolution invariance and
recovering re-ID oriented discriminative details. Our experiments on five
benchmark datasets confirm the effectiveness of our approach and its
superiority over the state-of-the-art methods, especially when the input
resolutions are unseen during training.
