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.

本研究提出一种新颖的方法，通过使用从可见图像中提取的属性来从输入的热图像中合成属性保留的可见图像，用于跨模态匹配和人脸验证，该方法组合了预先训练的VGG-Face网络和Attribute Preserved Generative Adversarial Network（AP-GAN）技术，并在ARL极化人脸数据集上进行实验，取得了明显的性能提升。

通过属性保持合成进行极化热到可见光人脸验证