Explainable Thermal to Visible Face Recognition Using Latent-Guided Generative Adversarial Network

Abstract

One of the main challenges in performing thermal-to-visible face image translation is preserving the identity across different spectral bands. Existing work does not effectively disentangle the identity from other confounding factors. In this paper, we propose a Latent-Guided Generative Adversarial Network (LG-GAN) to explicitly decompose an input image into identity code that is spectral-invariant and style code that is spectral-dependent. By using such a disentanglement, we are able to analyze the identity preservation by interpreting and visualizing the identity code. We present extensive face recognition experiments on two challenging Visible-Thermal face datasets. We show that the learned identity code is effective in preserving the identity, thus offering useful insights on interpreting and explaining thermal-to-visible face image translation.