The DOI matches its Crossref record.
The Effect of Image Quality and Forensic Expertise in Facial Image Comparisons
Abstract
Images of perpetrators in surveillance video footage are often used as evidence in court. In this study, identification accuracy was compared for forensic experts and untrained persons in facial image comparisons as well as the impact of image quality. Participants viewed thirty image pairs and were asked to rate the level of support garnered from their observations for concluding whether or not the two images showed the same person. Forensic experts reached their conclusions with significantly fewer errors than did untrained participants. They were also better than novices at determining when two high‐quality images depicted the same person. Notably, lower image quality led to more careful conclusions by experts, but not for untrained participants. In summary, the untrained participants had more false negatives and false positives than experts, which in the latter case could lead to a higher risk of an innocent person being convicted for an untrained witness.
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Experts perform notably better than members of the public at facial comparison, even when image quality is taken into consideration.
“Experts, in fact, perform notably better than members of the public [42], even when image quality was taken into consideration [132].”
From Forensic Facial Comparison: Current Status, Limitations, and Future Directions (2021) · cites this paper as [132]
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 [24]
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 [24]
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 [20]
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 [20]
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 [20]
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 [20]
Examiners show less impairment by image inversion compared to university students.
“Examiners also show less impairment by image inversion [20] in comparison to university students.”
From Error Rates in Users of Automatic Face Recognition Software (2015) · cites this paper as [20]
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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-citationError 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
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
Metadata
- Authors
- Norell, K., Läthén, K. B., Bergström, P., Rice, A., Natu, V., & O'Toole, A.
- Affiliations
- Forensic Audio and Digital Imaging Swedish National Laboratory of Forensic Science SE‐58194 Linkoping Sweden
- US Department of Defense School of Behavioral and Brain Sciences University of Texas at Dallas 800 W. Campbell Road Richardson Texas 75080‐3021
- Venue
- Journal of Forensic Sciences
- Publisher
- Wiley
- Published
- 2014-12-23
- Volume
- 60
- Issue
- 2
- Pages
- 331-340
- ISSN
- 0022-1198
- 1556-4029
- Cited by
- 31
- References
- 0
- Language
- en
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