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. 2022 Apr 5;7(1):30.
doi: 10.1186/s41235-022-00381-x.

Masked face identification is improved by diagnostic feature training

Daniel J Carragher  1   2 , Alice Towler  3 , Viktoria R Mileva  4 , David White  3 , Peter J B Hancock  4
Affiliations

Masked face identification is improved by diagnostic feature training

Daniel J Carragher et al. Cogn Res Princ Implic. .

Abstract

To slow the spread of COVID-19, many people now wear face masks in public. Face masks impair our ability to identify faces, which can cause problems for professional staff who identify offenders or members of the public. Here, we investigate whether performance on a masked face matching task can be improved by training participants to compare diagnostic facial features (the ears and facial marks)-a validated training method that improves matching performance for unmasked faces. We show this brief diagnostic feature training, which takes less than two minutes to complete, improves matching performance for masked faces by approximately 5%. A control training course, which was unrelated to face identification, had no effect on matching performance. Our findings demonstrate that comparing the ears and facial marks is an effective means of improving face matching performance for masked faces. These findings have implications for professions that regularly perform face identification.

Keywords: COVID-19; Face matching; Face recognition; Facial image comparison; Knowledge elicitation; Masks.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Examples of a match and b mismatch trials from the EFCT. Participants responded to the question “Is the same person shown in both photographs?” using 6 possible responses: “Definitely Not”, “Probably Not”, “Guess Not”, “Guess Yes”, “Probably Yes”, and “Definitely Yes”
Fig. 2
Fig. 2
Performance measures pre- and post-training for each training condition. a Area under the curve (AUC). b Response bias (criterion for declaring a match). c Sensitivity (d′), d overall accuracy (%). On all figures, unfilled circles represent individual data points (visualised in 1/30 bins by default), while the horizontal black lines represent the mean
Fig. 3
Fig. 3
Accuracy (%) on the EFCT for both training conditions on a) match trials b) and mismatch trials
See this image and copyright information in PMC

References

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