ArticleRoyal Society open science2020
Convolutional neural net face recognition works in non-human-like ways.
Article in Royal Society open science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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Who cites it
9 citing papers in PubMed, 29 citations in OpenAlex.
- Automated face recognition assists with low-prevalence face identity mismatches but can bias users.British journal of psychology (London, England : 1953) · 2026Article
- AI-augmented decision-making in face matching: comparing concurrent and non-concurrent advice presentation.Cognitive research: principles and implications · 2026Article
- Understanding the influence of design-related factors on human-AI teaming in a face matching task.Cognitive research: principles and implications · 2026Article
- Trust in automation and the accuracy of human-algorithm teams performing one-to-one face matching tasks.Cognitive research: principles and implications · 2024Article
- Distinct patterns of neural response to faces from different races in humans and deep networks.Social cognitive and affective neuroscience · 2023Article
- Varying sex and identity of faces affects face categorization differently in humans and computational models.Scientific reports · 2023Article
- Diverse types of expertise in facial recognition.Scientific reports · 2023Article
- The Oxford Face Matching Test: A non-biased test of the full range of individual differences in face perception.Behavior research methods · 2022Article
- Surgical face masks impair human face matching performance for familiar and unfamiliar faces.Cognitive research: principles and implications · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Convolutional neural networks (CNNs) give the state-of-the-art performance in many pattern recognition problems but can be fooled by carefully crafted patterns of noise. We report that CNN face recognition systems also make surprising 'errors'. We tested six commercial face recognition CNNs and found that they outperform typical human participants on standard face-matching tasks. However, they also declare matches that humans would not, where one image from the pair has been transformed to appear a different sex or race. This is not due to poor performance; the best CNNs perform almost perfectly on the human face-matching tasks, but also declare the most matches for faces of a different apparent race or sex. Although differing on the salience of sex and race, humans and computer systems are not working in completely different ways. They tend to find the same pairs of images difficult, suggesting some agreement about the underlying similarity space.
Indexed as
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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.