Recent advances in machine learning and computer vision have led to reported facial recognition accuracies surpassing human performance. We question if these systems will translate to real-world forensic scenarios in which a potentially low-resolution, low-quality, partially-occluded image is compared against a standard facial database. We describe the construction of a large-scale synthetic facial dataset along with a controlled facial forensic lineup, the combination of which allows for a controlled evaluation of facial recognition under a range of real-world conditions. Using this synthetic dataset, and a popular dataset of real faces, we evaluate the accuracy of two popular neural-based recognition systems. We find that previously reported face recognition accuracies of more than 95% drop to as low as 65% in this more challenging forensic scenario.
@article{arxiv.2311.06145,
title = {An Evaluation of Forensic Facial Recognition},
author = {Justin Norman and Shruti Agarwal and Hany Farid},
journal= {arXiv preprint arXiv:2311.06145},
year = {2023}
}