Thermal to Visible Facial Image Translation Using Generative Adversarial Networks

Zhongling Wang, Zhenzhong Chen, Feng Wu

Abstract: Thermal cameras can capture images invariant to illumination conditions. However, thermal facial images are difficult to be recognized by human examiners. In this letter, an end-to-end framework, which consists of a generative network and a detector network, is proposed to translate thermal facial images into visible ones. The generative network aims at generating visible images given the thermal ones. The detector can locate important facial landmarks on visible faces and help the generative network to generate more realistic images that are easier to be recognized. As demonstrated in the experiments, the faces generated by our method have good visual quality and maintain identity preserving features.