Face masks and fake masks: the effect of real and superimposed masks on face matching with super-recognisers, typical observers, and algorithms

Kay L Ritchie, Daniel J Carragher, Josh P Davis, Katie Read, Ryan E Jenkins, Eilidh Noyes, Katie L H Gray, Peter J B Hancock

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Mask wearing has been required in various settings since the outbreak of COVID-19, and research has shown that identity judgements are difficult for faces wearing masks. To date, however, the majority of experiments on face identification with masked faces tested humans and computer algorithms using images with superimposed masks rather than images of people wearing real face coverings. In three experiments we test humans (control participants and super-recognisers) and algorithms with images showing different types of face coverings. In all experiments we tested matching concealed or unconcealed faces to an unconcealed reference image, and we found a consistent decrease in face matching accuracy with masked compared to unconcealed faces. In Experiment 1, typical human observers were most accurate at face matching with unconcealed images, and poorer for three different types of superimposed mask conditions. In Experiment 2, we tested both typical observers and super-recognisers with superimposed and real face masks, and found that performance was poorer for real compared to superimposed masks. The same pattern was observed in Experiment 3 with algorithms. Our results highlight the importance of testing both humans and algorithms with real face masks, as using only superimposed masks may underestimate their detrimental effect on face identification.

Original languageEnglish
Article number5
Number of pages13
JournalCognitive research: principles and implications
Issue number1
Early online date2 Feb 2024
Publication statusE-pub ahead of print - 2 Feb 2024

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