# Deep Perceptual Mapping for Cross-Modal Face Recognition

arXiv: https://arxiv.org/abs/1601.05347 · HTML: https://mlsanity.com/paper/1601.05347/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2016.

## Built on by (most cited first, 5 of 5)

- [Beyond the Visible: A Survey on Cross-spectral Face Recognition](https://mlsanity.com/paper/2201.04435/) (2022)
- [Frequency Domain Modality-invariant Feature Learning for Visible-infrared Person Re-Identification](https://mlsanity.com/paper/2401.01839/) (2024)
- [Learning Domain and Pose Invariance for Thermal-to-Visible Face Recognition](https://mlsanity.com/paper/2211.09350/) (2022)
- [Rethinking the Domain Gap in Near-infrared Face Recognition](https://mlsanity.com/paper/2312.00627/) (2023)
- [Heterogeneous Visible-Thermal and Visible-Infrared Face Recognition using Unit-Class Loss and Cross-Modality Discriminator](https://mlsanity.com/paper/2111.14339/) (2021)

## Abstract (arXiv)

Cross modal face matching between the thermal and visible spectrum is a much desired 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 between 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 information. We show substantive performance improvement on three difficult thermal-visible face datasets. The presented approach improves the state-of-the-art by more than 10% on UND-X1 dataset and by more than 15-30% on NVESD dataset in terms of Rank-1 identification. Our method bridges the drop in performance due to the modality gap by more than 40%.