# Accuracy and Fairness of Facial Recognition Technology in Low-Quality Police Images: An Experiment With Synthetic Faces

arXiv: https://arxiv.org/abs/2505.14320 · HTML: https://mlsanity.com/paper/2505.14320/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 53 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 14 of 14)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [Interpreting the Latent Space of GANs for Semantic Face Editing](https://mlsanity.com/paper/1907.10786/) (2019)
- [Accuracy comparison across face recognition algorithms: Where are we on measuring race bias?](https://mlsanity.com/paper/1912.07398/) (2019)
- [Third Time's the Charm? Image and Video Editing with StyleGAN3](https://mlsanity.com/paper/2201.13433/) (2022)
- [GANDiffFace: Controllable Generation of Synthetic Datasets for Face Recognition with Realistic Variations](https://mlsanity.com/paper/2305.19962/) (2023)
- [Studying Bias in GANs through the Lens of Race](https://mlsanity.com/paper/2209.02836/) (2022)
- [Benchmarking Algorithmic Bias in Face Recognition: An Experimental Approach Using Synthetic Faces and Human Evaluation](https://mlsanity.com/paper/2308.05441/) (2023)
- [FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data](https://mlsanity.com/paper/2311.10476/) (2023)
- [Evaluating Proposed Fairness Models for Face Recognition Algorithms](https://mlsanity.com/paper/2203.05051/) (2022)
- [Pixel-Level Face Image Quality Assessment for Explainable Face Recognition](https://mlsanity.com/paper/2110.11001/) (2021)
- [Quantifying the Extent to Which Race and Gender Features Determine Identity in Commercial Face Recognition Algorithms](https://mlsanity.com/paper/2010.07979/) (2020)
- [The Impact of Balancing Real and Synthetic Data on Accuracy and Fairness in Face Recognition](https://mlsanity.com/paper/2409.02867/) (2024)
- [Examining Pathological Bias in a Generative Adversarial Network Discriminator: A Case Study on a StyleGAN3 Model](https://mlsanity.com/paper/2402.09786/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Lightweight Ensemble-Based Face Image Quality Assessment Method with Correlation-Aware Loss](https://mlsanity.com/paper/2509.10114/) (2025)

## Abstract (arXiv)

Facial recognition technology (FRT) is increasingly used in criminal investigations, yet most evaluations of its accuracy rely on high-quality images, unlike those often encountered by law enforcement. This study examines how five common forms of image degradation--contrast, brightness, motion blur, pose shift, and resolution--affect FRT accuracy and fairness across demographic groups. Using synthetic faces generated by StyleGAN3 and labeled with FairFace, we simulate degraded images and evaluate performance using Deepface with ArcFace loss in 1:n identification tasks. We perform an experiment and find that false positive rates peak near baseline image quality, while false negatives increase as degradation intensifies--especially with blur and low resolution. Error rates are consistently higher for women and Black individuals, with Black females most affected. These disparities raise concerns about fairness and reliability when FRT is used in real-world investigative contexts. Nevertheless, even under the most challenging conditions and for the most affected subgroups, FRT accuracy remains substantially higher than that of many traditional forensic methods. This suggests that, if appropriately validated and regulated, FRT should be considered a valuable investigative tool. However, algorithmic accuracy alone is not sufficient: we must also evaluate how FRT is used in practice, including user-driven data manipulation. Such cases underscore the need for transparency and oversight in FRT deployment to ensure both fairness and forensic validity.