# Deep Perceptual Mapping for Thermal to Visible Face Recognition

arXiv: https://arxiv.org/abs/1507.02879 · HTML: https://mlsanity.com/paper/1507.02879/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2015.

## Built on by (most cited first, 3 of 3)

- [Learning Domain and Pose Invariance for Thermal-to-Visible Face Recognition](https://mlsanity.com/paper/2211.09350/) (2022)
- [IFAST: Weakly Supervised Interpretable Face Anti-spoofing from Single-shot Binocular NIR Images](https://mlsanity.com/paper/2309.17399/) (2023)
- [Augmentation techniques for video surveillance in the visible and thermal spectral range](https://mlsanity.com/paper/2606.13042/) (2026)

## Abstract (arXiv)

Cross modal face matching between the thermal and visible spectrum is a much de- sired capability for night-time surveillance and security applications. Due to a very large modality gap, thermal-to-visible face recognition is one of the most challenging face matching problem. In this paper, we present an approach to bridge this modality gap by a significant margin. Our approach captures the highly non-linear relationship be- tween the two modalities by using a deep neural network. Our model attempts to learn a non-linear mapping from visible to thermal spectrum while preserving the identity in- formation. We show substantive performance improvement on a difficult thermal-visible face dataset. The presented approach improves the state-of-the-art by more than 10% in terms of Rank-1 identification and bridge the drop in performance due to the modality gap by more than 40%.