  Thermal facial imagery offers valuable insight into physiological states such
as inflammation and stress by detecting emitted radiation in the infrared
spectrum, which is unseen in the visible spectra. Telemedicine applications
could benefit from thermal imagery, but conventional computers are reliant on
RGB cameras and lack thermal sensors. As a result, we propose the
Visible-to-Thermal Facial GAN (VTF-GAN) that is specifically designed to
generate high-resolution thermal faces by learning both the spatial and
frequency domains of facial regions, across spectra. We compare VTF-GAN against
several popular GAN baselines and the first conditional Denoising Diffusion
Probabilistic Model (DDPM) for VT face translation (VTF-Diff). Results show
that VTF-GAN achieves high quality, crisp, and perceptually realistic thermal
faces using a combined set of patch, temperature, perceptual, and Fourier
Transform losses, compared to all baselines including diffusion.
