# Polarimetric Thermal to Visible Face Verification via Self-Attention Guided Synthesis

arXiv: https://arxiv.org/abs/1904.07344 · HTML: https://mlsanity.com/paper/1904.07344/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2019.

## Built on by (most cited first, 19 of 19)

- [A Large-Scale, Time-Synchronized Visible and Thermal Face Dataset](https://mlsanity.com/paper/2101.02637/) (2021)
- [T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2209.08814/) (2022)
- [Prepended Domain Transformer: Heterogeneous Face Recognition without Bells and Whistles](https://mlsanity.com/paper/2210.06529/) (2022)
- [Simultaneous Face Hallucination and Translation for Thermal to Visible Face Verification using Axial-GAN](https://mlsanity.com/paper/2104.06534/) (2021)
- [A Synthesis-Based Approach for Thermal-to-Visible Face Verification](https://mlsanity.com/paper/2108.09558/) (2021)
- [Beyond the Visible: A Survey on Cross-spectral Face Recognition](https://mlsanity.com/paper/2201.04435/) (2022)
- [Recent Advances in Deep Learning Techniques for Face Recognition](https://mlsanity.com/paper/2103.10492/) (2021)
- [Towards NIR-VIS Masked Face Recognition](https://mlsanity.com/paper/2104.06761/) (2021)
- [Heterogeneous Face Frontalization via Domain Agnostic Learning](https://mlsanity.com/paper/2107.08311/) (2021)
- [A Brief Survey on Person Recognition at a Distance](https://mlsanity.com/paper/2212.08969/) (2022)
- [ProS: Facial Omni-Representation Learning via Prototype-based Self-Distillation](https://mlsanity.com/paper/2311.01929/) (2023)
- [Learning Domain and Pose Invariance for Thermal-to-Visible Face Recognition](https://mlsanity.com/paper/2211.09350/) (2022)
- [CM-Diff: A Single Generative Network for Bidirectional Cross-Modality Translation Diffusion Model Between Infrared and Visible Images](https://mlsanity.com/paper/2503.09514/) (2025)
- [Thermal to Visible Image Synthesis under Atmospheric Turbulence](https://mlsanity.com/paper/2204.03057/) (2022)
- [Multi-Domain Biometric Recognition using Body Embeddings](https://mlsanity.com/paper/2503.10931/) (2025)
- [Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination](https://mlsanity.com/paper/2203.16669/) (2022)
- [Cross-Spectral Attention for Unsupervised RGB-IR Face Verification and Person Re-identification](https://mlsanity.com/paper/2411.19215/) (2024)
- [LaPIG: Cross-Modal Generation of Paired Thermal and Visible Facial Images](https://mlsanity.com/paper/2503.16376/) (2025)
- [Multi-Attribute guided Thermal Face Image Translation based on Latent Diffusion Model](https://mlsanity.com/paper/2512.21032/) (2025)

## Abstract (arXiv)

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.