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Passport Officers’ Errors in Face Matching

Overview of attention for article published in PLOS ONE, August 2014
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Title
Passport Officers’ Errors in Face Matching
Published in
PLOS ONE, August 2014
DOI 10.1371/journal.pone.0103510
Pubmed ID
Authors

David White, Richard I. Kemp, Rob Jenkins, Michael Matheson, A. Mike Burton

Abstract

Photo-ID is widely used in security settings, despite research showing that viewers find it very difficult to match unfamiliar faces. Here we test participants with specialist experience and training in the task: passport-issuing officers. First, we ask officers to compare photos to live ID-card bearers, and observe high error rates, including 14% false acceptance of 'fraudulent' photos. Second, we compare passport officers with a set of student participants, and find equally poor levels of accuracy in both groups. Finally, we observe that passport officers show no performance advantage over the general population on a standardised face-matching task. Across all tasks, we observe very large individual differences: while average performance of passport staff was poor, some officers performed very accurately--though this was not related to length of experience or training. We propose that improvements in security could be made by emphasising personnel selection.

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X Demographics

The data shown below were collected from the profiles of 195 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 237 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
Italy 1 <1%
Austria 1 <1%
Australia 1 <1%
United Kingdom 1 <1%
New Zealand 1 <1%
Poland 1 <1%
Unknown 230 97%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 51 22%
Student > Ph. D. Student 46 19%
Student > Master 27 11%
Researcher 21 9%
Other 11 5%
Other 40 17%
Unknown 41 17%
Readers by discipline Count As %
Psychology 132 56%
Computer Science 12 5%
Medicine and Dentistry 7 3%
Agricultural and Biological Sciences 6 3%
Unspecified 6 3%
Other 26 11%
Unknown 48 20%