ArticlePloS one2015
Error Rates in Users of Automatic Face Recognition Software.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed.
- Do evaluative statements in facial identification overstate the strength of the evidence?Journal of forensic sciences · 2026Article
- Masked and elusive: congruency fails in video-to-photo face matching.Cognitive research: principles and implications · 2026Article
- Testing the effects of presentation modality and presence of similarity percentages in the automatic image recognition system in Italy.Frontiers in psychology · 2026Article
- Cognitive Bias Affects Perception and Decision-Making in Simulated Facial Recognition Searches.Behavioral sciences (Basel, Switzerland) · 2025Article
- Jack of all trades, master of one: domain-specific and domain-general contributions to perceptual expertise in visual comparison.Cognitive research: principles and implications · 2024Article
- A guide to measuring expert performance in forensic pattern matching.Behavior research methods · 2024Article
- Challenges of accreditation in forensic fields concerned with human identification: a survey of European facial examiners.Forensic sciences research · 2024Article
- Trust in automation and the accuracy of human-algorithm teams performing one-to-one face matching tasks.Cognitive research: principles and implications · 2024Article
- A Clinical Trial Evaluating the Efficacy of Deep Learning-Based Facial Recognition for Patient Identification in Diverse Hospital Settings.Bioengineering (Basel, Switzerland) · 2024Article
- Application of artificial intelligence to eyewitness identification.Cognitive research: principles and implications · 2024Article
- The effect of fingerprint expertise on visual short-term memory.Cognitive research: principles and implications · 2024Article
- Diverse types of expertise in facial recognition.Scientific reports · 2023Article
- Selecting police super-recognisers.PloS one · 2023Article
- Match me if you can: Evidence for a domain-general visual comparison ability.Psychonomic bulletin & review · 2022Article
- COVID-19 masks increase the influence of face recognition algorithm decisions on human decisions in unfamiliar face matching.PloS one · 2022Article
- Review
- Differences between and within individuals, and subprocesses of face cognition: implications for theory, research and personnel selection.Royal Society open science · 2020Article
- UNSW Face Test: A screening tool for super-recognizers.PloS one · 2020Article
- Human-algorithm teaming in face recognition: How algorithm outcomes cognitively bias human decision-making.PloS one · 2020Article
- Interpol review of imaging and video 2016-2019.Forensic science international. Synergy · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, wide deployment of automatic face recognition systems has been accompanied by substantial gains in algorithm performance. However, benchmarking tests designed to evaluate these systems do not account for the errors of human operators, who are often an integral part of face recognition solutions in forensic and security settings. This causes a mismatch between evaluation tests and operational accuracy. We address this by measuring user performance in a face recognition system used to screen passport applications for identity fraud. Experiment 1 measured target detection accuracy in algorithm-generated 'candidate lists' selected from a large database of passport images. Accuracy was notably poorer than in previous studies of unfamiliar face matching: participants made over 50% errors for adult target faces, and over 60% when matching images of children. Experiment 2 then compared performance of student participants to trained passport officers-who use the system in their daily work-and found equivalent performance in these groups. Encouragingly, a group of highly trained and experienced "facial examiners" outperformed these groups by 20 percentage points. We conclude that human performance curtails accuracy of face recognition systems-potentially reducing benchmark estimates by 50% in operational settings. Mere practise does not attenuate these limits, but superior performance of trained examiners suggests that recruitment and selection of human operators, in combination with effective training and mentorship, can improve the operational accuracy of face recognition systems.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.