  Polarimetric thermal to visible face verification entails matching two images
that contain significant domain differences. Several recent approaches have
attempted to synthesize visible faces from thermal images for cross-modal
matching. In this paper, we take a different approach in which rather than
focusing only on synthesizing visible faces from thermal faces, we also propose
to synthesize thermal faces from visible faces. Our intuition is based on the
fact that thermal images also contain some discriminative information about the
person for verification. Deep features from a pre-trained Convolutional Neural
Network (CNN) are extracted from the original as well as the synthesized
images. These features are then fused to generate a template which is then used
for verification. The proposed synthesis network is based on the self-attention
generative adversarial network (SAGAN) which essentially allows efficient
attention-guided image synthesis. Extensive experiments on the ARL polarimetric
thermal face dataset demonstrate that the proposed method achieves
state-of-the-art performance.
