  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 learn
domain-invariant robust features from these modalities for cross-modal
matching. In this paper, we use attributes extracted from visible images to
synthesize attribute-preserved visible images from thermal imagery for
cross-modal matching. A pre-trained attribute predictor network is used to
extract the attributes from the visible image. Then, a novel multi-scale
generator is proposed to synthesize the visible image from the thermal image
guided by the extracted attributes. Finally, a pre-trained VGG-Face network is
leveraged to extract features from the synthesized image and the input visible
image for verification. Extensive experiments evaluated on three datasets (ARL
Face Database, Visible and Thermal Paired Face Database, and Tufts Face
Database) demonstrate that the proposed method achieves state-of-the-art
performance. In particular, it achieves around 2.41\%, 2.85\% and 1.77\%
improvements in Equal Error Rate (EER) over the state-of-the-art methods on the
ARL Face Database, Visible and Thermal Paired Face Database, and Tufts Face
Database, respectively. An extended dataset (ARL Face Dataset volume III)
consisting of polarimetric thermal faces of 121 subjects is also introduced in
this paper. Furthermore, an ablation study is conducted to demonstrate the
effectiveness of different modules in the proposed method.
