Accuracy and fairness of facial recognition technology in real-world, low-quality police images: An experiment

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 how that varies by gender and race. Using synthetic faces generated by StyleGAN3 and labeled with FairFace, we simulate degraded images and evaluate performance using the DeepFace framework with the ArcFace model in 1:n identification tasks. By using simulated faces, we are able to estimate conditional FPIR and FNIR error-rate curves across degradation levels under controlled conditions, providing practical guidance on how database search performance changes as questioned image quality deteriorates. We find that high motion blur and contrast and low resolution and brightness substantially increase FNIR. At the selected operating point, FPIR is substantially higher than FNIR and is highest near baseline image quality, generally decreasing as image degradation increases and demographic disparities persist across degradation conditions. Demographic disparities vary by error type: FPIR is often higher for Black individuals, particularly Black women, whereas the demographic groups with the highest FNIR differ across degradation conditions. These disparities raise concerns about fairness and reliability of the use of FRT. These findings highlight the importance of evaluating algorithmic performance using the low-quality images encountered in criminal investigations to ensure both fairness and validity.