Polarimetric Thermal to Visible Face Verification via Attribute Preserved Synthesis
This addresses cross-modal face verification for security applications, but it is incremental as it builds on existing synthesis and attribute-based approaches.
The paper tackles thermal to visible face verification by synthesizing visible images from thermal inputs while preserving attributes extracted from visible images, achieving significant improvements over state-of-the-art methods on the ARL Polarimetric face dataset.
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.