# GAN-based Deidentification of Drivers' Face Videos: An Assessment of Human Factors Implications in NDS Data

arXiv: https://arxiv.org/abs/2306.02374 · HTML: https://mlsanity.com/paper/2306.02374/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 4 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [FSGAN: Subject Agnostic Face Swapping and Reenactment](https://mlsanity.com/paper/1908.05932/) (2019)
- [SimSwap: An Efficient Framework For High Fidelity Face Swapping](https://mlsanity.com/paper/2106.06340/) (2021)
- [DeepPrivacy: A Generative Adversarial Network for Face Anonymization](https://mlsanity.com/paper/1909.04538/) (2019)

## Abstract (arXiv)

This paper addresses the problem of sharing drivers' face videos for transportation research while adhering to proper ethical guidelines. The paper first gives an overview of the multitude of problems associated with sharing such data and then proposes a framework on how artificial intelligence-based techniques, specifically face swapping, can be used for de-identifying drivers' faces. Through extensive experimentation with an Oak Ridge National Laboratory (ORNL) dataset, we demonstrate the effectiveness of face-swapping algorithms in preserving essential attributes related to human factors research, including eye movements, head movements, and mouth movements. The efficacy of the framework was also tested on various naturalistic driving study data collected at the Virginia Tech Transportation Institute. The results achieved through the proposed techniques were evaluated qualitatively and quantitatively using various metrics. Finally, we discuss possible measures for sharing the de-identified videos with the greater research community.