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Unfamiliar face matching: Pairs out-perform individuals and provide a route to training
Abstract
Matching unfamiliar faces is known to be difficult. Here, we ask whether performance can be improved by asking viewers to work in pairs, a manipulation known to increase accuracy for low‐level visual discrimination tasks. Across four experiments we consistently find that face matching accuracy is higher for pairs of viewers than for individuals. This ‘pairs advantage’ is generally driven by adopting the response of the higher scoring partner. However, when the task becomes difficult, both partners' performance is improved by working in a pair. In two experiments, we find evidence that working in a pair can lead to subsequent improvements in individual performance, specifically for viewers whose accuracy is initially low. The pairs' technique therefore offers the opportunity for substantial improvements in face matching performance, along with an added training benefit.
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Research by Dowsett and Burton [25] shows that working collaboratively on facial comparison decisions with high-performing individuals improves the accuracy of low-performers in subsequent tests.
“Supporting this, research by Dowsett and Burton [25] shows that working collaboratively on facial comparison decisions with high-performing individuals improves the accuracy of low-performers in subsequent tests.”
From Do professional facial image comparison training courses work? (2019) · cites this paper as [25]
Facial examiners' training also includes extensive on-the-job mentoring from more senior analysts, which may be critical in developing their expertise.
“Second, facial examiners’ training also includes extensive on-the-job mentoring from more senior analysts, which may be critical in developing their expertise.”
From Do professional facial image comparison training courses work? (2019) · cites this paper as [25]
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Cited by
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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
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
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
Self-citationPsychophysical Evaluation of Human Performance in Detecting Digital Face Image Manipulations
Nichols, R., Rathgeb, C., Drozdowski, P., & Busch, C. (2022) · IEEE Access · doi:10.1109/access.2022.3160596 · Cited by 16
Unfamiliar face matching with photographs of infants and children
Kramer, R. S. S., Mulgrew, J., & Reynolds, M. G. (2018) · PeerJ · doi:10.7717/peerj.5010 · Cited by 15
Metadata
- Authors
- Dowsett, A. J., & Burton, A. M.
- Affiliations
- School of Psychology University of Aberdeen UK
- Venue
- British Journal of Psychology
- Publisher
- Wiley
- Published
- 2014-11-13
- Volume
- 106
- Issue
- 3
- Pages
- 433-445
- ISSN
- 0007-1269
- 2044-8295
- Cited by
- 77
- References
- 0
- Language
- en
- Funders
- European Research Council (ERC-ADG 323262)
- Economic and Social Research Council (ES/J022950/1)
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