Cited by 121

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Perceptual expertise in forensic facial image comparison

White, David, Phillips, P. Jonathon, Hahn, Carina A., Hill, Matthew, O'Toole, Alice J.·2015·Proceedings of the Royal Society B: Biological Sciences

Abstract

Forensic facial identification examiners are required to match the identity of faces in images that vary substantially, owing to changes in viewing conditions and in a person's appearance. These identifications affect the course and outcome of criminal investigations and convictions. Despite calls for research on sources of human error in forensic examination, existing scientific knowledge of face matching accuracy is based, almost exclusively, on people without formal training. Here, we administered three challenging face matching tests to a group of forensic examiners with many years' experience of comparing face images for law enforcement and government agencies. Examiners outperformed untrained participants and computer algorithms, thereby providing the first evidence that these examiners are experts at this task. Notably, computationally fusing responses of multiple experts produced near-perfect performance. Results also revealed qualitative differences between expert and non-expert performance. First, examiners' superiority was greatest at longer exposure durations, suggestive of more entailed comparison in forensic examiners. Second, experts were less impaired by image inversion than non-expert students, contrasting with face memory studies that show larger face inversion effects in high performers. We conclude that expertise in matching identity across unfamiliar face images is supported by processes that differ qualitatively from those supporting memory for individual faces.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1098/rspb.2015.1292

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • These possibilities do not account for the qualitative differences observed in their performance in previous work.

    “However, these possibilities do not account for the qualitative differences observed in their performance in previous work [20, 22].”

    From Do professional facial image comparison training courses work? (2019) · cites this paper as [22]

    The Discussion (page unavailable) identifies qualitative differences in forensic examiners’ performance and says they suggest a contribution from differences in cognitive processing. It also says motivation alone has limited explanatory power, but does not establish that all the unspecified ‘possibilities’ cannot account for those differences. (LLM, conf=0.78)

    Experimental — accuracy not yet measured
  • Facial examiners are a specialist group of facial image comparison practitioners who conduct slow, systematic, feature-based comparisons, often as part of criminal investigations.

    “Facial examiners are a specialist group of facial image comparison practitioners who conduct slow, systematic, feature-based comparisons, often as part of criminal investigations (see [20, 22, 23]).”
  • Facial examiners-who receive extensive training in feature comparison-tend to outperform novices.

    “It is also consistent with the fact that facial examiners—who receive extensive training in feature comparison—tend to outperform novices (e.g., [20, 22–24]).”
  • Recent studies show that facial examiners are more accurate than novices on face identification tasks.

    “Recent studies show that facial examiners are more accurate than novices on face identification tasks [20, 22–24].”
  • In previous work, examiners' accuracy was less impaired when face images were presented upside down compared to novices.

    “and their accuracy was less impaired when face images were presented upside down compared to novices [20, 22].”

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 33

  1. 10.1109/MMUL.2012.4 · DOI · Paper page
  2. 10.1007/978-0-387-71041-9_21 · DOI
  3. 10.1016/j.imavis.2013.12.002 · DOI
  4. 10.1098/rstb.2014.0254 · DOI · Paper page
  5. 10.1126/science.1111565 · DOI
  6. 10.1002/(SICI)1099-0720(199706)11:3<211::AID-ACP430>3.0.CO;2-O · DOI
  7. 10.1037/1076-898X.5.4.339 · DOI · Paper page
  8. 10.3758/BRM.42.1.286 · DOI · Paper page
  9. 10.1111/1467-9280.00144 · DOI · Paper page
  10. 10.1371/journal.pone.0103510 · DOI · Paper page
  11. 10.1111/1556-4029.12660 · DOI · Paper page
  12. 10.1016/j.scijus.2008.10.011 · DOI · Paper page
  13. 10.6028/NIST.IR.7758 · DOI
  14. O'Toole AJ, 2012, Comparing face recognition algorithms to humans on challenging tasks, ACM. Appl. Percept., 9, 16
  15. 10.1177/0956797613492986 · DOI
  16. 10.1111/j.2044-8295.1988.tb02747.x · DOI
  17. 10.1126/science.831281 · DOI
  18. 10.1098/rstb.1992.0012 · DOI
  19. 10.1037/0096-3445.115.2.107 · DOI
  20. 10.1016/j.neuropsychologia.2011.05.002 · DOI
  21. 10.3758/PBR.16.2.252 · DOI · Paper page
  22. 10.3758/BF03193433 · DOI · Paper page
  23. 10.1016/j.visres.2011.08.009 · DOI
  24. O'Toole AJ, 2007, Fusing face-verification algorithms and humans, IEEE. Trans. Syst, 37, 1149
  25. 10.1002/acp.2971 · DOI · Paper page
  26. 10.1148/radiol.2422051997 · DOI
  27. 10.1371/journal.pone.0114759 · DOI
  28. 10.1016/j.tics.2010.12.001 · DOI
  29. 10.1016/S1364-6613(02)01903-4 · DOI
  30. 10.1080/00140137908924617 · DOI · Paper page
  31. Sternberg RJ, 1996, The road to excellence: the acquisition of expert performance in the arts and sciences, sports, and games, 347
  32. Edmond G, 2014, How to cross-examine forensic scientists: a guide for lawyers, Aust. Bar Rev, 39, 174
  33. 10.1177/0956797611414729 · DOI

Cited by

Sorted by citations

Metadata

Authors
White, D., Phillips, P. J., Hahn, C. A., Hill, M., & O'Toole, A. J.
Affiliations
  • School of Psychology, The University of New South Wales, Sydney, New South Wales 2052, Australia
  • National Institute of Standards and Technology, 100 Bureau Drive, MS 8940, Gaithersburg, MD 20899, USA
  • The University of Texas at Dallas, Richardson, TX 75080, USA
Venue
Proceedings of the Royal Society B: Biological Sciences
Published
2015-09-07
Volume
282
Issue
1814
Pages
20151292
ISSN
  • 0962-8452
  • 1471-2954
Cited by
121
References
2
Language
en

About these checks

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.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review