The DOI matches its Crossref record.
Perceived ability and actual recognition accuracy for unfamiliar and famous faces
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Facial comparison accuracy and data quality are directly correlated.
“Facial comparison accuracy and data quality are, thus, directly correlated [95,96], especially in terms of individual accuracy variation across multiple analysts [97] and individual analyst ability overestimation [98].”
From Forensic Facial Comparison: Current Status, Limitations, and Future Directions (2021) · cites this paper as [98]
The citation [98] is attached to the point about individual ability overestimation, not the earlier claim about data quality. The paper’s Discussion (§5; page unavailable) says observers’ initial judgments of their ability to recognize unfamiliar faces poorly predict their accuracy. It does not establish that the observers were analysts or that they specifically overestimated their ability rather than misjudged it in either direction. (LLM, conf=0.84)
Experimental — accuracy not yet measuredHighly pixelated images can reduce face matching abilities by up to 50% in trained individuals when compared to a high-quality image sample.
“Highly pixelated images can reduce face matching abilities by up to 50% in trained individuals when compared to a high-quality image sample [94,98].”
From Forensic Facial Comparison: Current Status, Limitations, and Future Directions (2021) · cites this paper as [98]
The Introduction reports that face-matching performance declines when picture quality is degraded. It does not give a reduction of up to 50% or establish that this result applies to trained individuals; the paper’s own experiments compare same-image and different-image line-ups, not pixelated and high-quality images. (LLM, conf=0.88)
Experimental — accuracy not yet measuredPeople have very little insight into their own face identification ability.
“In general, people have very little insight into their own face identification ability [31–34]”
From Do professional facial image comparison training courses work? (2019) · cites this paper as [31]
The General discussion reports that people’s ability judgements poorly predicted their performance identifying unfamiliar faces. But the Introduction states that observers could predict their ability to recognize famous faces, so the source does not support the claim without that distinction. (LLM, conf=0.88)
Experimental — accuracy not yet measured
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Research works printed but not linked: 32
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Cited by
Sorted by citations
Four principles of explainable artificial intelligence
Phillips, P. J., Hahn, C. A., Fontana, P. C., Yates, A. N., Greene, K., Broniatowski, D. A., & Przybocki, M. A. (2021) · doi:10.6028/nist.ir.8312 · Cited by 262
Four Principles of Explainable Artificial Intelligence
Phillips, P. J., Hahn, C. A., Fontana, P. C., Broniatowski, D. A., & Przybocki, M. A. (2020) · doi:10.6028/nist.ir.8312-draft · Cited by 136
Do professional facial image comparison training courses work?
Towler, A., Kemp, R. I., Burton, A. M., Dunn, J. D., Wayne, T., Moreton, R., & White, D. (2019) · PLOS ONE · doi:10.1371/journal.pone.0211037 · Cited by 111
Forensic Facial Comparison: Current Status, Limitations, and Future Directions
Bacci, N., Davimes, J. G., Steyn, M., & Briers, N. (2021) · Biology · doi:10.3390/biology10121269 · Cited by 26
Metadata
- Authors
- Bindemann, M., Attard, J., & Johnston, R. A.
- Affiliations
- School of Psychology, University of Kent, Canterbury CT2 7NP, UK
- Venue
- Cogent Psychology
- Publisher
- Informa UK Limited
- Published
- 2014-12-04
- Volume
- 1
- Issue
- 1
- Pages
- 986903
- ISSN
- 2331-1908
- Cited by
- 29
- References
- 0
- Language
- en
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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