# Multi-Metric Evaluation of Thermal-to-Visual Face Recognition

arXiv: https://arxiv.org/abs/2007.11987 · HTML: https://mlsanity.com/paper/2007.11987/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [When Visible-to-Thermal Facial GAN Beats Conditional Diffusion](https://mlsanity.com/paper/2302.09395/) (2023)
- [Generating Thermal Human Faces for Physiological Assessment Using Thermal Sensor Auxiliary Labels](https://mlsanity.com/paper/2106.08091/) (2021)

## Abstract (arXiv)

In this paper, we aim to address the problem of heterogeneous or cross-spectral face recognition using machine learning to synthesize visual spectrum face from infrared images. The synthesis of visual-band face images allows for more optimal extraction of facial features to be used for face identification and/or verification. We explore the ability to use Generative Adversarial Networks (GANs) for face image synthesis, and examine the performance of these images using pre-trained Convolutional Neural Networks (CNNs). The features extracted using CNNs are applied in face identification and verification. We explore the performance in terms of acceptance rate when using various similarity measures for face verification.