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

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