# An Evaluation of Forensic Facial Recognition

arXiv: https://arxiv.org/abs/2311.06145 · HTML: https://mlsanity.com/paper/2311.06145/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 10 Nov 2023.

## Builds on (most cited first, 7 of 7)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Deep Face Recognition: A Survey](https://mlsanity.com/paper/1804.06655/) (2018)
- [SynFace: Face Recognition with Synthetic Data](https://mlsanity.com/paper/2108.07960/) (2021)
- [DigiFace-1M: 1 Million Digital Face Images for Face Recognition](https://mlsanity.com/paper/2210.02579/) (2022)
- [Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing](https://mlsanity.com/paper/2001.00964/) (2020)

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

- [Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumvention](https://mlsanity.com/paper/2405.01820/) (2024)
- [Does Head Pose Correction Improve Biometric Facial Recognition?](https://mlsanity.com/paper/2512.03199/) (2025)

## Abstract (arXiv)

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.