Polarimetric Thermal to Visible Face Verification via Self-Attention Guided Synthesis
This work addresses cross-modal face verification for security applications, offering an incremental improvement by incorporating bidirectional synthesis to enhance discriminative information.
The paper tackles polarimetric thermal to visible face verification by proposing a bidirectional synthesis approach that generates both visible faces from thermal images and thermal faces from visible images, leveraging self-attention GANs for synthesis and feature fusion, achieving state-of-the-art performance on the ARL dataset.
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.