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Evaluating Training Methods for Facial Image Comparison: The Face Shape Strategy Does Not Work
Abstract
Human performance on unfamiliar face matching is known to be highly error prone. However, in organisations where staff are required to perform this task as part of their daily work, attempts are often made to mitigate risk by providing training. Importantly, the methods used in these training courses have not been subjected to empirical validation. In this study we evaluate a common component of many training programmes which encourages viewers to classify face shape. Our results show very low agreement in face shape classification, both within and between participants, and across repeated presentations of a single image to a single participant. Furthermore, face shape classification training did not improve face matching accuracy, suggesting that the face shape strategy does not facilitate identification.
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Following Towler, White and Kemp [17], the GFMT was split into two equally difficult 20-item versions (10 match, 10 non-match), and allocation of each version to the pre- and post-training was counterbalanced across participants.
“Following Towler, White and Kemp [17], the GFMT was split into two equally difficult 20-item versions (10 match, 10 non-match). Allocation of each version to the pre- and post-training was counterbalanced across participants.”
From Do professional facial image comparison training courses work? (2019) · cites this paper as [17]
Some recommended methods have been shown to be ineffective in improving accuracy.
“Indeed, some recommended methods have been shown to be ineffective in improving accuracy [41, 42].”
From Error Rates in Users of Automatic Face Recognition Software (2015) · cites this paper as [42]
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- 10.1002/acp.2968 · DOI · Paper page
- 10.1037/1076-898X.5.4.339 · DOI · Paper page
- 10.3758/BRM.42.1.286 · DOI · Paper page
- 10.1037/h0031619 · DOI · Paper page
- 10.1016/j.cognition.2011.08.001 · DOI
- 10.1002/(SICI)1099-0720(199706)11:3<211::AID-ACP430>3.0.CO;2-O · DOI
- 10.1007/s00414-010-0446-4 · DOI
- 10.1007/978-3-642-01793-3_17 · DOI
- 10.3758/s13423-013-0475-3 · DOI · Paper page
- 10.1080/00140137908924617 · DOI · Paper page
Cited by
Sorted by citations
Young, 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
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Metadata
- Authors
- Towler, A., White, D., & Kemp, R. I.
- Affiliations
- School of Psychology, The University of New South Wales, Sydney, NSW 2052, Australia
- Venue
- Perception
- Publisher
- SAGE Publications
- Published
- 2014-01-01
- Volume
- 43
- Issue
- 2-3
- Pages
- 214-218
- ISSN
- 0301-0066
- 1468-4233
- Cited by
- 35
- References
- 0
- Language
- en
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