  Designing face recognition systems that are capable of matching face images
obtained in the thermal spectrum with those obtained in the visible spectrum is
a challenging problem. In this work, we propose the use of semantic-guided
generative adversarial network (SG-GAN) to automatically synthesize visible
face images from their thermal counterparts. Specifically, semantic labels,
extracted by a face parsing network, are used to compute a semantic loss
function to regularize the adversarial network during training. These semantic
cues denote high-level facial component information associated with each pixel.
Further, an identity extraction network is leveraged to generate multi-scale
features to compute an identity loss function. To achieve photo-realistic
results, a perceptual loss function is introduced during network training to
ensure that the synthesized visible face is perceptually similar to the target
visible face image. We extensively evaluate the benefits of individual loss
functions, and combine them effectively to learn the mapping from thermal to
visible face images. Experiments involving two multispectral face datasets show
that the proposed method achieves promising results in both face synthesis and
cross-spectral face matching.
