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.

翻译：现有的热到可见面对面的核查方法预期热到可见的表面图像将具有类似的分辨率。 在现实世界的远程监视系统中,这是不太可能的,因为人类距离摄像机很远。为了解决这个问题,我们提出从低分辨率热图像进行热到可见的面部核查的任务。此外,我们提议Axial-General Adversarial网络(Axial-GAN)将高分辨率可见的图像合成为匹配。在拟议的方法中,我们用xial-ative 注意层来扩大GAN框架,利用变压器最近的进展来模拟远程依赖性。我们通过对两种不同的热到可见的表面数据集进行评估来展示拟议方法的有效性。与相关的最新工程相比,我们的结果显示图像质量和面部验证性能都有显著改善,而且效率也大大提高。