# Polarimetric Thermal to Visible Face Verification via Attribute Preserved Synthesis

## TL;DR

This paper introduces a novel method for thermal to visible face verification by synthesizing attribute-preserved visible images from thermal images using a specialized GAN, improving cross-modal matching accuracy.

## Contribution

The paper proposes a new Attribute Preserved GAN that leverages visible image attributes to synthesize more accurate visible faces from thermal images for verification.

## Key findings

- Significant improvement over state-of-the-art methods on ARL Polarimetric face dataset.
- Effective preservation of attributes enhances cross-modal face verification.
- The method outperforms existing synthesis and matching techniques.

## Abstract

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.