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Feature instructions improve face-matching accuracy
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Training effects for unfamiliar face matching show dissociations: instructions to attend to some features improve match trials, while attention to other features improves mismatch trials.
“Similar dissociations are seen in training effects for unfamiliar face matching, where instructions to attend to some features (e.g., the eyebrow regions) yields improved performance on match trials (Megreya & Bindemann, 2018), while increased attention to other features (e.g., facial marks such as moles and blemishes) improves performance on mismatch trials (Towler et al., 2021).”
From A criterion-placement theory of face matching (2026) · cites this paper as Megreya & Bindemann, 2018
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Research works printed but not linked: 44
- PJB Hancock, 2000, Recognition of unfamiliar faces, Trends Cog Sci, 4, 330, 10.1016/S1364-6613(00)01519-9 · DOI · Paper page
- AM Burton, 2011, The Oxford handbook of face perception, 287
- RA Johnston, 2009, Familiar and unfamiliar face recognition: A review, Memory, 17, 577, 10.1080/09658210902976969 · DOI
- M Bindemann, 2010, Finding needles in haystacks: Identity mismatch frequency and facial identity verification, J Exp Psychol Appl, 16, 378, 10.1037/a0021893 · DOI
- AM Burton, 2010, The Glasgow Face Matching Test, Behav Res Methods, 42, 286, 10.3758/BRM.42.1.286 · DOI · Paper page
- AM Megreya, 2006, Unfamiliar faces are not faces: Evidence from a matching task, Mem & Cognit, 34, 865, 10.3758/BF03193433 · DOI · Paper page
- R Kemp, 1997, When seeing should not be believing: Photographs, credit cards and fraud, Appl Cognitive Psych, 11, 211, 10.1002/(SICI)1099-0720(199706)11:3<211::AID-ACP430>3.0.CO;2-O · DOI
- AM Megreya, 2008, Matching faces to photographs: Poor performance in eyewitness memory (without the memory), J Exp Psychol Appl, 14, 364, 10.1037/a0013464 · DOI
- D White, 2014, Passport officers’ errors in face matching, PLoS ONE, 9, e103510, 10.1371/journal.pone.0103510 · DOI · Paper page
- AM Megreya, 2013, Matching face images taken on the same day or months apart: The limitations of photo-ID, Appl Cognitive Psych, 27, 700, 10.1002/acp.2965 · DOI · Paper page
- KL Ritchie, 2017, Learning faces from variability, Q J Exp Psychol, 70, 897, 10.1080/17470218.2015.1136656 · DOI
- S Andrews, 2015, Telling faces together: Learning new faces through exposure to multiple instances, Q J Exp Psychol, 68, 2041, 10.1080/17470218.2014.1003949 · DOI
- BE Wirth, 2017, An easy game for frauds? Effects of professional experience and time pressure on passport-matching performance, J Exp Psychol Appl, 23, 138, 10.1037/xap0000114 · DOI · Paper page
- DJ Robertson, 2015, From policing to passport control: The limitations of photo ID, Keesing: The Journal of Documents and Identity, 46, 3
- HM Alenezi, 2013, The effect of feedback on face matching accuracy, Appl Cognitive Psych, 27, 735, 10.1002/acp.2968 · DOI · Paper page
- D White, 2014, Feedback training for facial image comparison, Psychon B Rev, 21, 100, 10.3758/s13423-013-0475-3 · DOI · Paper page
- M Bindemann, 2011, Me, myself, and I: Different recognition rates for three photo-IDs of the same person, Perception, 40, 625, 10.1068/p7008 · DOI
- N Menon, 2015, Variation in photos of the same face drives improvements in identity verification, Perception, 44, 1332, 10.1177/0301006615599902 · DOI
- D White, 2014, Redesigning photo-ID to improve unfamiliar face matching performance, J Exp Psychol Appl, 20, 166, 10.1037/xap0000009 · DOI
- AJ Dowsett, 2015, Unfamiliar face matching: Pairs out-perform individuals and provide a route to training, Br J Psychol, 106, 433, 10.1111/bjop.12103 · DOI · Paper page
- D White, 2013, Crowd effects in unfamiliar face matching, Appl Cognitive Psych, 27, 769, 10.1002/acp.2971 · DOI · Paper page
- AM Burton, 2013, Why has research in face recognition progressed so slowly? The importance of variability, Q J Exp Psychol, 66, 1467, 10.1080/17470218.2013.800125 · DOI
- AM Burton, 2005, Robust representations for face recognition: The power of averages, Cogn Psychol, 51, 256, 10.1016/j.cogpsych.2005.06.003 · DOI
- AM Megreya, 2007, Hits and false positives in face matching: A familiarity-based dissociation, Percept Psychophys, 69, 1175, 10.3758/BF03193954 · DOI · Paper page
- RGM Armann, 2016, A familiarity disadvantage for remembering specific images of faces, J Exp Psychol Hum Percept Perform, 42, 571, 10.1037/xhp0000174 · DOI
- Y Konar, 2010, Holistic processing is not correlated with face-identification accuracy, Psychol Sci, 21, 38, 10.1177/0956797609356508 · DOI
- A Towler, 2017, Evaluating the feature comparison strategy for forensic face identification, J Exp Psychol Appl, 23, 47, 10.1037/xap0000108 · DOI · Paper page
- C Coin, 1997, Encoding activity and face recognition, Memory, 5, 545, 10.1080/741941479 · DOI
- PG Schyns, 2002, Show me the features! Understanding recognition from the use of visual information, Psychol Sci, 13, 402, 10.1111/1467-9280.00472 · DOI
- J Sadr, 2003, The role of eyebrows in face recognition, Perception, 32, 285, 10.1068/p5027 · DOI
- A Abaza, 2013, A survey on ear biometrics, ACM Computing Surveys (CSUR), 45, 22, 10.1145/2431211.2431221 · DOI · Paper page
- CA Meissner, 2001, Thirty years of investigating the own-race bias in memory for faces: A meta-analytic review, Psychol Public Policy Law, 7, 3, 10.1037/1076-8971.7.1.3 · DOI · Paper page
- AM Megreya, 2009, Revisiting the processing of internal and external features of unfamiliar faces: The headscarf effect, Perception, 38, 1831, 10.1068/p6385 · DOI
- AM Megreya, 2011, The other race effect does not rely on memory: Evidence from a matching task, Q J Exp Psychol, 64, 1473, 10.1080/17470218.2011.575228 · DOI · Paper page
- C Michel, 2006, Same-race faces are perceived more holistically than other-race faces, Vis Cogn, 14, 55, 10.1080/13506280500158761 · DOI
- C Michel, 2006, Holistic processing is finely tuned for faces of one’s own race, Psychol Sci, 17, 608, 10.1111/j.1467-9280.2006.01752.x · DOI
- JW Tanaka, 1993, Parts and wholes in face recognition, Q J Exp Psychol, 46A, 225, 10.1080/14640749308401045 · DOI
- J Tanaka, 2004, A holistic account of the own-race effect in face recognition: Evidence from a cross-cultural study, Cognition, 93, B1, 10.1016/j.cognition.2003.09.011 · DOI
- PJ Hills, 2013, Eye-tracking the own-race bias in face recognition: Revealing the perceptual and socio-cognitive mechanisms, Cognition, 129, 586, 10.1016/j.cognition.2013.08.012 · DOI
- PJ Hills, 2013, Removing the own-race bias in face recognition by attentional shift using fixation crosses to diagnostic features: An eye-tracking study, Vis Cogn, 21, 876, 10.1080/13506285.2013.834016 · DOI
- N Abudarham, 2016, Reverse engineering the face space: Discovering the critical features for face identification, J Vis, 16, 40, 10.1167/16.3.40 · DOI
- V Bruce, 1999, Verification of face identities from images captured on video, J Exp Psychol Appl, 5, 339, 10.1037/1076-898X.5.4.339 · DOI · Paper page
- AM Burton, 2016, Identity from variation: Representations of faces derived from multiple instances, Cogn Sci, 40, 202, 10.1111/cogs.12231 · DOI
- R Jenkins, 2011, Variability in photos of the same face, Cognition, 121, 313, 10.1016/j.cognition.2011.08.001 · DOI
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Forensic 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
Validation of forensic facial comparison by morphological analysis in photographic and CCTV samples
Bacci, N., Houlton, T. M. R., Briers, N., & Steyn, M. (2021) · International Journal of Legal Medicine · doi:10.1007/s00414-021-02512-3 · Cited by 23
A criterion-placement theory of face matching
Baker, K. A., Mondloch, C. J., Hancock, P. J. B., & Bindemann, M. (2026) · Cognition · doi:10.1016/j.cognition.2025.106319 · Cited by 5
Self-citation
Metadata
- Authors
- Megreya, A. M., & Bindemann, M.
- ORCID
- Megreya, Ahmed M.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-03-15
- Volume
- 13
- Issue
- 3
- Pages
- e0193455
- ISSN
- 1932-6203
- PMC
- PMC5854257
- Cited by
- 36
- References
- 0
- Language
- en
- Funders
- Qatar National Research Fund (NPRP9-272-5-033)
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