# A Comparison of Human and Machine Learning Errors in Face Recognition

arXiv: https://arxiv.org/abs/2502.11337 · HTML: https://mlsanity.com/paper/2502.11337/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 53 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Human uncertainty makes classification more robust](https://mlsanity.com/paper/1908.07086/) (2019)
- [A Light CNN for Deep Face Representation with Noisy Labels](https://mlsanity.com/paper/1511.02683/) (2015)
- [When combinations of humans and AI are useful: A systematic review and meta-analysis](https://mlsanity.com/paper/2405.06087/) (2024)
- [Towards Unbiased and Accurate Deferral to Multiple Experts](https://mlsanity.com/paper/2102.13004/) (2021)
- [Who Should Predict? Exact Algorithms For Learning to Defer to Humans](https://mlsanity.com/paper/2301.06197/) (2023)
- [Face.evoLVe: A High-Performance Face Recognition Library](https://mlsanity.com/paper/2107.08621/) (2021)
- [Learning to Defer with Limited Expert Predictions](https://mlsanity.com/paper/2304.07306/) (2023)
- [Differences between human and machine perception in medical diagnosis](https://mlsanity.com/paper/2011.14036/) (2020)
- [Stating Comparison Score Uncertainty and Verification Decision Confidence Towards Transparent Face Recognition](https://mlsanity.com/paper/2210.10354/) (2022)
- [Gender Stereotyping Impact in Facial Expression Recognition](https://mlsanity.com/paper/2210.05332/) (2022)
- [A View From Somewhere: Human-Centric Face Representations](https://mlsanity.com/paper/2303.17176/) (2023)
- [How Accurate Does It Feel? -- Human Perception of Different Types of Classification Mistakes](https://mlsanity.com/paper/2302.06413/) (2023)
- [Inclusive Portraits: Race-Aware Human-in-the-Loop Technology](https://mlsanity.com/paper/2311.03567/) (2023)
- [Face Recognition: to Deploy or not to Deploy? A Framework for Assessing the Proportional Use of Face Recognition Systems in Real-World Scenarios](https://mlsanity.com/paper/2402.05731/) (2024)

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

- [Improving Model Safety by Targeted Error Correction](https://mlsanity.com/paper/2605.02544/) (2026)

## Abstract (arXiv)

Machine learning applications in high-stakes scenarios should always operate under human oversight. Developing an optimal combination of human and machine intelligence requires an understanding of their complementarities, particularly regarding the similarities and differences in the way they make mistakes. We perform extensive experiments in the area of face recognition and compare two automated face recognition systems against human annotators through a demographically balanced user study. Our research uncovers important ways in which machine learning errors and human errors differ from each other, and suggests potential strategies in which human-machine collaboration can improve accuracy in face recognition.