  Thermal images reveal medically important physiological information about
human stress, signs of inflammation, and emotional mood that cannot be seen on
visible images. Providing a method to generate thermal faces from visible
images would be highly valuable for the telemedicine community in order to show
this medical information. To the best of our knowledge, there are limited works
on visible-to-thermal (VT) face translation, and many current works go the
opposite direction to generate visible faces from thermal surveillance images
(TV) for law enforcement applications. As a result, we introduce favtGAN, a VT
GAN which uses the pix2pix image translation model with an auxiliary sensor
label prediction network for generating thermal faces from visible images.
Since most TV methods are trained on only one data source drawn from one
thermal sensor, we combine datasets from faces and cityscapes. These combined
data are captured from similar sensors in order to bootstrap the training and
transfer learning task, especially valuable because visible-thermal face
datasets are limited. Experiments on these combined datasets show that favtGAN
demonstrates an increase in SSIM and PSNR scores of generated thermal faces,
compared to training on a single face dataset alone.
