# Thermal to Visible Synthesis of Face Images using Multiple Regions

arXiv: https://arxiv.org/abs/1803.07599 · HTML: https://mlsanity.com/paper/1803.07599/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2018.

## Built on by (most cited first, 9 of 9)

- [Simultaneous Face Hallucination and Translation for Thermal to Visible Face Verification using Axial-GAN](https://mlsanity.com/paper/2104.06534/) (2021)
- [T-FAKE: Synthesizing Thermal Images for Facial Landmarking](https://mlsanity.com/paper/2408.15127/) (2024)
- [Heterogeneous Face Frontalization via Domain Agnostic Learning](https://mlsanity.com/paper/2107.08311/) (2021)
- [Learning Domain and Pose Invariance for Thermal-to-Visible Face Recognition](https://mlsanity.com/paper/2211.09350/) (2022)
- [Thermal to Visible Image Synthesis under Atmospheric Turbulence](https://mlsanity.com/paper/2204.03057/) (2022)
- [Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination](https://mlsanity.com/paper/2203.16669/) (2022)
- [A Bidirectional Conversion Network for Cross-Spectral Face Recognition](https://mlsanity.com/paper/2205.01595/) (2022)
- [Multi-Attribute guided Thermal Face Image Translation based on Latent Diffusion Model](https://mlsanity.com/paper/2512.21032/) (2025)
- [Frequency Routing and Identity Conditioning for Thermal-to-Visible Face Translation](https://mlsanity.com/paper/2610.17859/) (2026)

## Abstract (arXiv)

Synthesis of visible spectrum faces from thermal facial imagery is a promising approach for heterogeneous face recognition; enabling existing face recognition software trained on visible imagery to be leveraged, and allowing human analysts to verify cross-spectrum matches more effectively. We propose a new synthesis method to enhance the discriminative quality of synthesized visible face imagery by leveraging both global (e.g., entire face) and local regions (e.g., eyes, nose, and mouth). Here, each region provides (1) an independent representation for the corresponding area, and (2) additional regularization terms, which impact the overall quality of synthesized images. We analyze the effects of using multiple regions to synthesize a visible face image from a thermal face. We demonstrate that our approach improves cross-spectrum verification rates over recently published synthesis approaches. Moreover, using our synthesized imagery, we report the results on facial landmark detection-commonly used for image registration-which is a critical part of the face recognition process.