  Existing thermal-to-visible face verification approaches expect the thermal
and visible face images to be of similar resolution. This is unlikely in
real-world long-range surveillance systems, since humans are distant from the
cameras. To address this issue, we introduce the task of thermal-to-visible
face verification from low-resolution thermal images. Furthermore, we propose
Axial-Generative Adversarial Network (Axial-GAN) to synthesize high-resolution
visible images for matching. In the proposed approach we augment the GAN
framework with axial-attention layers which leverage the recent advances in
transformers for modelling long-range dependencies. We demonstrate the
effectiveness of the proposed method by evaluating on two different
thermal-visible face datasets. When compared to related state-of-the-art works,
our results show significant improvements in both image quality and face
verification performance, and are also much more efficient.
