  Thermal to visible face verification is a challenging problem due to the
large domain discrepancy between the modalities. Existing approaches either
attempt to synthesize visible faces from thermal faces or extract robust
features from these modalities for cross-modal matching. In this paper, we take
a different approach in which we make use of the attributes extracted from the
visible image to synthesize the attribute-preserved visible image from the
input thermal image for cross-modal matching. A pre-trained VGG-Face network is
used to extract the attributes from the visible image. Then, a novel Attribute
Preserved Generative Adversarial Network (AP-GAN) is proposed to synthesize the
visible image from the thermal image guided by the extracted attributes.
Finally, a deep network is used to extract features from the synthesized image
and the input visible image for verification. Extensive experiments on the ARL
Polarimetric face dataset show that the proposed method achieves significant
improvements over the state-of-the-art methods.
