Cited by 24

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Improving face identification with specialist teams

Balsdon, Tarryn, Summersby, Stephanie, Kemp, Richard I., White, David·2018·Cognitive Research: Principles and Implications

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 · 10.1186/s41235-018-0114-7

Cites

1 of 1 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?

  • Recruiting people with natural aptitude in face identification tasks has been shown to produce substantial boosts to accuracy.

    “Recruiting people with natural aptitude in face identification tasks [37, 38]”

    From Do professional facial image comparison training courses work? (2019) · cites this paper as [37]

    The Discussion reports that selecting high performers using face-identification tests improved accuracy by about 7%, a gain the authors describe as modest rather than substantial. The Background also cautions that the stronger performance of existing specialist teams cannot be attributed to recruitment alone because their selection and training processes are not documented. (LLM, conf=0.88)

    Checked by a reasoning model — may be wrong.

    Experimental — accuracy not yet measured

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: 42

  1. Bindemann, M., Avetisyan, M., & Rakow, T. (2012). Who can recognize unfamiliar faces? Individual differences and observer consistency in person identification. Journal of Experimental Psychology: Applied, 18(3), 277–291.
  2. Bobak, A. K., Bennetts, R. J., Parris, B. A., Jansari, A., & Bate, S. (2016). An in-depth cognitive examination of individuals with superior face recognition skills. Cortex, 82, 48–62. · DOI
  3. Bobak, A. K., Dowsett, A. J., & Bate, S. (2016). Solving the border control problem: evidence of enhanced face matching in individuals with extraordinary face recognition skills. PLoS One, 11(2), e0148148. · DOI · Paper page
  4. Bruce, V., Henderson, Z., Newman, C., & Burton, A. M. (2001). Matching identities of familiar and unfamiliar faces caught on CCTV images. Journal of Experimental Psychology: Applied, 7(3), 207–218.
  5. Burton, A. M., White, D., & McNeill, A. (2010). The Glasgow face matching test. Behavior Research Methods, 42(1), 286–291 https://doi.org/10.3758/BRM.42.1.286. · DOI · Paper page
  6. Danileiko, I., & Lee, M. D. (in press). A model-based approach to the wisdom of the crowd in category learning. Cognitive Science https://doi.org/10.1111/cogs.12561. · DOI
  7. Davis, J. P., Lander, K., Evans, R., & Jansari, A. (2016). Investigating predictors of superior face recognition ability in police super-recognisers. Applied Cognitive Psychology, 30, 827–840. · DOI
  8. Dowsett, A. J., & Burton, A. M. (2015). Unfamiliar face matching: pairs out-perform individuals and provide a route to training. British Journal of Psychology, 106(3), 433–445. · DOI · Paper page
  9. Dowsett, A. J., Sandford, A., & Burton, A. M. (2016). Face learning with multiple images leads to fast acquisition of familiarity for specific individuals. The Quarterly Journal of Experimental Psychology, 69(1), 1–10. · DOI
  10. Duchaine, B., & Nakayama, K. (2006). The Cambridge face memory test: results for neurologically intact individuals and an investigation of its validity using inverted face stimuli and prosopagnosic participants. Neuropsychologia, 22, 576–585 https://doi.org/10.1016/j.neuropsychologia.2005.07.001. · DOI
  11. Grother, P., & Ngan, M. (2014). Face Recognition Vendor Test (FRVT). NIST Interagency Report(8009). Retrieved from: http://biometrics.nist.gov/cs_links/face/frvt/frvt2013/NIST_8009.pdf . Accessed June 2018. · DOI · Paper page
  12. Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning. New York: Springer Verlag. · DOI · Paper page
  13. Hu, Y., Jackson, K., Yates, A., White, D., Phillips, P. J., & O’Toole, A. J. (2017). Person identification: qualitative differences in reliance on face and body in forensic facial examiners and untrained people. Visual Cognition, 25, 492–506. · DOI
  14. Jeckeln, G., Hahn, C. A., Noyes, E., Cavazos, J. G., & O'Toole, A. J. (in press). Wisdom of the social versus non-social crowd in face identification. British Journal of Psychology.
  15. Kantner, J., & Lindsay, D. S. (2012). Response bias in recognition memory as a cognitive trait. Memory & Cognition, 40(8), 1163–1177. · DOI
  16. Kantner, J., & Lindsay, D. S. (2014). Cross-situational consistency in recognition memory response bias. Psychonomic Bulletin & Review, 21(5), 1272–1280. · DOI
  17. Krause, S., James, R., Faria, J. J., Ruxton, G. D., & Krause, J. (2011). Swarm intelligence in humans: diversity can trump ability. Animal Behaviour, 81(5), 941–948. https://doi.org/10.1016/j.anbehav.2010.12.018 . · DOI
  18. Megreya, A. M., Sandford, A., & Burton, A. M. (2013). Matching face images taken on the same day or months apart: the limitations of photo ID. Applied Cognitive Psychology, 27, 700–706. · DOI · Paper page
  19. Mollon, J. D., Bosten, J. M., Peterzell, D. H., & Webster, M. A. (2017). Individual differences in visual science: what can be learned and what is good experimental practice? Vision Research, 141, 4–15. · DOI
  20. Moore, R. M., & Johnston, R. A. (2013). Motivational incentives improve unfamiliar face matching accuracy. Applied Cognitive Psychology, 27(6), 754–760. · DOI
  21. Motta-Mena, N., Elbich, D., Duchaine, B., & Scherf, K. S. (2017). The female Cambridge face memory test (F-CFMT+). Journal of Vision, 17(10), 841–841. · DOI
  22. Murphy, J., Ipser, A., Gaigg, S. B., & Cook, R. (2015). Exemplar variance supports robust learning of facial identity. Journal of Experimental Psychology: Human Perception and Performance, 41(3), 577–558.
  23. Norell, K., Brorsson Lathen, K., Bergstrom, P., Rice, A., Natu, V., & O'Toole, A. J. (2015). The effect of image quality and forensic expertise in facial image comparisons. Journal of Forensic Sciences, 60, 331–340. · DOI · Paper page
  24. Noyes, E., Phillips, P. J., & O’Toole, A. J. (2017). What is a super-recogniser? In M. Bindermann, & A. M. Megreya (Eds.), Face processing: systems, disorders, and cultural differences. New York: Nova.
  25. O'Toole, A. J., Abdi, H., Jiang, F., & Phillips, P. J. (2007). Fusing face-verification algorithms and humans. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 37, 1149–1155. · DOI
  26. O'Toole, A. J., An, X., Dunlop, J., Natu, V., & Phillips, P. J. (2012). Comparing face recognition algorithms to humans on challenging tasks. ACM Transactions on Applied Perception (TAP), 9(4), 16.
  27. Richler, J. J., Floyd, R. J., & Gauthier, I. (2014). The Vanderbilt holistic face processing test: a short and reliable measure of holistic face processing. Journal of Vision, 14(11), 10. · DOI
  28. Ritchie, K. L., & Burton, A. M. (2017). Learning faces from variability. The Quarterly Journal of Experimental Psychology, 70(5), 897–905. · DOI
  29. Robertson, D. J., Noyes, E., Dowsett, A. J., Jenkins, R., & Burton, A. M. (2016). Face recognition by metropolitan police super-recognisers. PLoS One, 11, e0150036. · DOI
  30. Russell, R., Duchaine, B., & Nakayama, K. (2009). Super-recognizers: people with extraordinary face recognition ability. Psychonomic Bulletin and Review, 16, 252–257. · DOI · Paper page
  31. Shah, P., Gaule, A., Sowden, S., Bird, G., & Cook R. (2015). The 20-item prosopagnosia index (PI20): a self-report instrument for identifying developmental prosopagnosia. Royal Society Open Science, 2(6),140343. https://doi.org/ https://doi.org/10.1098/rsos.140343 · DOI
  32. Shah, P., Sowden, S., Gaule, A., Catmur, C., & Bird, G. (2015). The 20 item prosopagnosia index (PI20): relationship with the Glasgow face-matching test. Royal Society Open Science, 2(11), 150305. · DOI
  33. Shakeshaft, N. G., & Plomin, R. (2015). Genetic specificity of face recognition. Proceedings of the National Academy of Sciences, 112, 12887–12892. · DOI
  34. Towler, A., White, D., & Kemp, R. I. (2017). Evaluating the feature comparison strategy for forensic face identification. Journal of Experimental Psychology: Applied, 23, 47–58.
  35. White, D., Burton, A. L., & Kemp, R. I. (2016). Not looking yourself: the cost of self-selecting photographs for identity verification. British Journal of Psychology, 107(2), 359–373. · DOI
  36. White, D., Burton, A. M., Jenkins, R., & Kemp, R. I. (2014). Redesigning photo-ID to improve unfamiliar face matching performance. Journal of Experimental Psychology: Applied, 20(2), 166.
  37. White, D., Burton, A. M., Kemp, R. I., & Jenkins, R. (2013). Crowd effects in unfamiliar face matching. Applied Cognitive Psychology, 27, 769–777. · DOI · Paper page
  38. White, D., Kemp, R. I., Jenkins, R., Matheson, M., & Burton, A. M. (2014). Passport officers' errors in face matching. PLoS One, 9(8), 1–6.
  39. White, D., Phillips, P. J., Hahn, C. A., Hill, M., & O'Toole, A. J. (2015). Perceptual expertise in forensic facial image comparison. Proceedings of the Royal Society of London B: Biological Sciences, 282, 1814–1822. · DOI · Paper page
  40. White, D., Rivolta, D., Burton, A. M., Al-Janabi, S., & Palermo, R. (2017). Face matching impairment in developmental prosopagnosia. The Quarterly Journal of Experimental Psychology, 70(2), 287–297. · DOI
  41. Wilmer, J. B., Germine, L., Chabris, C. F., Chatterjee, G., Williams, M., Loken, E., … Duchaine, B. (2010). Human face recognition ability is specific and highly heritable. Proceedings of the National Academy of Sciences, 107, 5238–5241. · DOI · Paper page
  42. Wirth, B. E., & Carbon, C. C. (2017). An easy game for frauds? Effects of professional experience and time pressure on passport-matching performance. Journal of Experimental Psychology: Applied, 23, 138–157.

Cited by

Sorted by citations

Metadata

Authors
Balsdon, T., Summersby, S., Kemp, R. I., & White, D.
Venue
Cognitive Research: Principles and Implications
Published
2018-06-27
Volume
3
Issue
1
ISSN
2365-7464
Cited by
24
References
1
Language
en
Funders
Australian Research Council (LP130100702, LP160101523)

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