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
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.
Integrity signals
Registry
Cites
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.
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 measuredFacial 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]).”
From Do professional facial image comparison training courses work? (2019) · cites this paper as [22]
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]).”
From Do professional facial image comparison training courses work? (2019) · cites this paper as [22]
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].”
From Do professional facial image comparison training courses work? (2019) · cites this paper as [22]
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].”
From Do professional facial image comparison training courses work? (2019) · cites this paper as [22]
The superior accuracy experienced by facial examiners is typically accompanied by qualitative differences in their performance.
“However, the superior accuracy experienced by facial examiners is typically accompanied by qualitative differences in their performance.”
From Do professional facial image comparison training courses work? (2019) · cites this paper as [22]
Facial examiners show smaller face inversion effects than novices.
“Importantly, these experts also show qualitative differences in their performance: extracting more diagnostic identity information from facial features than novices [20], and showing smaller face inversion effects [20, 22] (see S3 Appendix for details of the inversion effect).”
From Do professional facial image comparison training courses work? (2019) · cites this paper as [22]
Similar groups of professional examiners have been shown to have superior face identification abilities.
“In recent studies, similar groups of professional examiners have been shown to have superior face identification abilities [20, 21].”
From Error Rates in Users of Automatic Face Recognition Software (2015) · cites this paper as [21]
Trained facial examiners have outperformed control participants both here and in recent studies.
“Notwithstanding, trained facial examiners have outperformed control participants both here and in recent studies [20, 21].”
From Error Rates in Users of Automatic Face Recognition Software (2015) · cites this paper as [21]
In recent studies, facial examiners were more accurate in one-to-one facial image comparison compared to control groups.
“In recent studies, facial examiners were more accurate in one-to-one facial image comparison compared to control groups [20, 21] and so we expected that examiners would outperform the other groups.”
From Error Rates in Users of Automatic Face Recognition Software (2015) · cites this paper as [21]
Course D did not produce the qualitative changes associated with expertise in facial image comparison: increased diagnosticity of facial feature similarity ratings and a reduced inversion effect.
“In addition, Course D did not produce the qualitative changes associated with expertise in facial image comparison: increased diagnosticity of facial feature similarity ratings and a reduced inversion effect [20, 22].”
From Do professional facial image comparison training courses work? (2019) · cites this paper as [22]
Expertise of examiners is most evident at longer exposure durations and when comparing high-quality images.
“For instance, expertise of examiners is most evident at longer exposure durations [20] and when comparing high-quality images [21].”
From Error Rates in Users of Automatic Face Recognition Software (2015) · cites this paper as [21]
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
Strengthening Forensic Science in the United States: A Path Forward
Council, N. R. (2009) · CrimRxiv · doi:10.21428/cb6ab371.b2d683d2 · Cited by 1,461
Accuracy and reliability of forensic latent fingerprint decisions
Ulery, B. T., Hicklin, R. A., Buscaglia, J., & Roberts, M. A. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1018707108 · Cited by 350
Flagged first, then by citations
Research works printed but not linked: 33
- 10.1109/MMUL.2012.4 · DOI · Paper page
- 10.1007/978-0-387-71041-9_21 · DOI
- 10.1016/j.imavis.2013.12.002 · DOI
- 10.1098/rstb.2014.0254 · DOI · Paper page
- 10.1126/science.1111565 · DOI
- 10.1002/(SICI)1099-0720(199706)11:3<211::AID-ACP430>3.0.CO;2-O · DOI
- 10.1037/1076-898X.5.4.339 · DOI · Paper page
- 10.3758/BRM.42.1.286 · DOI · Paper page
- 10.1111/1467-9280.00144 · DOI · Paper page
- 10.1371/journal.pone.0103510 · DOI · Paper page
- 10.1111/1556-4029.12660 · DOI · Paper page
- 10.1016/j.scijus.2008.10.011 · DOI · Paper page
- 10.6028/NIST.IR.7758 · DOI
- O'Toole AJ, 2012, Comparing face recognition algorithms to humans on challenging tasks, ACM. Appl. Percept., 9, 16
- 10.1177/0956797613492986 · DOI
- 10.1111/j.2044-8295.1988.tb02747.x · DOI
- 10.1126/science.831281 · DOI
- 10.1098/rstb.1992.0012 · DOI
- 10.1037/0096-3445.115.2.107 · DOI
- 10.1016/j.neuropsychologia.2011.05.002 · DOI
- 10.3758/PBR.16.2.252 · DOI · Paper page
- 10.3758/BF03193433 · DOI · Paper page
- 10.1016/j.visres.2011.08.009 · DOI
- O'Toole AJ, 2007, Fusing face-verification algorithms and humans, IEEE. Trans. Syst, 37, 1149
- 10.1002/acp.2971 · DOI · Paper page
- 10.1148/radiol.2422051997 · DOI
- 10.1371/journal.pone.0114759 · DOI
- 10.1016/j.tics.2010.12.001 · DOI
- 10.1016/S1364-6613(02)01903-4 · DOI
- 10.1080/00140137908924617 · DOI · Paper page
- Sternberg RJ, 1996, The road to excellence: the acquisition of expert performance in the arts and sciences, sports, and games, 347
- Edmond G, 2014, How to cross-examine forensic scientists: a guide for lawyers, Aust. Bar Rev, 39, 174
- 10.1177/0956797611414729 · DOI
Cited by
Sorted by citations
Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms
Phillips, P. J., Yates, A. N., Hu, Y., Hahn, C. A., Noyes, E., Jackson, K., Cavazos, J. G., Jeckeln, G., Ranjan, R., Sankaranarayanan, S., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1721355115 · Cited by 461
Self-citationYoung, A. W., & Burton, A. M. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.11.007 · Cited by 235
Error Rates in Users of Automatic Face Recognition Software
White, D., Dunn, J. D., Schmid, A. C., & Kemp, R. I. (2015) · PLOS ONE · doi:10.1371/journal.pone.0139827 · Cited by 141
Self-citationDo 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
Self-citationThinking forensics: Cognitive science for forensic practitioners
Edmond, G., Towler, A., Growns, B., Ribeiro, G., Found, B., White, D., Ballantyne, K., Searston, R. A., Thompson, M. B., Tangen, J. M., et al. (2017) · Science & Justice · doi:10.1016/j.scijus.2016.11.005 · Cited by 95
Self-citationForensic 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
Growns, B., & Kukucka, J. (2021) · Applied Cognitive Psychology · doi:10.1002/acp.3800 · Cited by 20
Finding Makhubu: A morphological forensic facial comparison
Houlton, T. M. R., & Steyn, M. (2018) · Forensic Science International · doi:10.1016/j.forsciint.2018.01.022 · Cited by 13
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
- Publisher
- The Royal Society
- 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