# Deep Cross Polarimetric Thermal-to-visible Face Recognition

arXiv: https://arxiv.org/abs/1801.01486 · HTML: https://mlsanity.com/paper/1801.01486/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2018.

## Built on by (most cited first, 4 of 4)

- [Simultaneous Face Hallucination and Translation for Thermal to Visible Face Verification using Axial-GAN](https://mlsanity.com/paper/2104.06534/) (2021)
- [Beyond the Visible: A Survey on Cross-spectral Face Recognition](https://mlsanity.com/paper/2201.04435/) (2022)
- [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)

## Abstract (arXiv)

In this paper, we present a deep coupled learning frame- work to address the problem of matching polarimetric ther- mal face photos against a gallery of visible faces. Polariza- tion state information of thermal faces provides the miss- ing textural and geometrics details in the thermal face im- agery which exist in visible spectrum. we propose a coupled deep neural network architecture which leverages relatively large visible and thermal datasets to overcome the problem of overfitting and eventually we train it by a polarimetric thermal face dataset which is the first of its kind. The pro- posed architecture is able to make full use of the polari- metric thermal information to train a deep model compared to the conventional shallow thermal-to-visible face recogni- tion methods. Proposed coupled deep neural network also finds global discriminative features in a nonlinear embed- ding space to relate the polarimetric thermal faces to their corresponding visible faces. The results show the superior- ity of our method compared to the state-of-the-art models in cross thermal-to-visible face recognition algorithms.