Cross-Spectrum Thermal Face Pattern Generator

Conversion of a visible face image into a thermal face image (V2T), or one thermal face image into another one given a different target temperature (T2T), is required in applications such as thermography, human body thermal pattern analysis, and surveillance using cross-spectral imaging. In this work, we propose to use conditional generative adversarial networks (cGAN) with cGAN loss, perceptual loss, and temperature loss to solve the conversion tasks. In our experiment, we used Carl and SpeakingFaces Databases. Frèchet Inception Distance (FID) is used to evaluate the generated images. As well, face recognition was applied to assess the performance of our models. For the V2T task, the FID of the generated thermal images reached a low value of 57.3. For the T2T task, we achieved a rank-1 face recognition rate of 91.0% which indicates that the generated thermal images preserve the majority of the identity information.