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

Generating Thermal Human Faces for Physiological Assessment Using Thermal Sensor Auxiliary Labels