Hyper-realistic face masks: a new challenge in person identification

Sanders, Jet G.; Ueda, Yasuo; Minemoto, Kazusa; Noyes, Eilidh; Yoshikawa, Sakiko; Jenkins, Rob

doi:10.1186/s41235-017-0079-y

Cited by 16 publications

(27 citation statements)

References 42 publications

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“…The use of the mask was discovered only when the perpetrator decided to remove it mid-flight (Zamost, 2010 ). Despite this report occurring almost a decade ago, and with other such reports also occurring ( see Bernstein, 2010 ), a recent study by Sanders et al ( 2017 ) provided the first assessment of participants’ ability to detect the use of a hyper-realistic silicone mask as a method of physical identity deception. Their research showed that only 6% of participants detected that a confederate was wearing a mask at spontaneous or prompted report, and this rose to just 57% when asked directly.…”

Section: Discussionmentioning

confidence: 89%

Detecting morphed passport photos: a training and individual differences approach

et al. 2018

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Our reliance on face photos for identity verification is at odds with extensive research which shows that matching pairs of unfamiliar faces is highly prone to error. This process can therefore be exploited by identity fraudsters seeking to deceive ID checkers (e.g., using a stolen passport which contains an image of a similar looking individual to deceive border control officials). In this study we build on previous work which sought to quantify the threat posed by a relatively new type of fraud: morphed passport photos. Participants were initially unaware of the presence of morphs in a series of face photo arrays and were simply asked to detect which images they thought had been digitally manipulated (i.e., “images that didn’t look quite right”). All participants then received basic information on morph fraud and rudimentary guidance on how to detect such images, followed by a morph detection training task (Training Group, n = 40), or a non-face control task (Guidance Group, n = 40). Participants also completed a post-guidance/training morph detection task and the Models Face Matching Test (MFMT). Our findings show that baseline morph detection rates were poor, that morph detection training significantly improved the identification of these images over and above basic guidance, and that accuracy in the mismatch condition of the MFMT correlated with morph detection ability. The results are discussed in relation to potential countermeasures for morph-based identity fraud.Electronic supplementary materialThe online version of this article (10.1186/s41235-018-0113-8) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 89%

Detecting morphed passport photos: a training and individual differences approach

et al. 2018

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“…Nevertheless, it almost certainly overestimates the rate of spontaneous detection when a mask framing is absent. Sanders et al ( 2017 ) reported extremely low rates of spontaneous detection, both for photographic presentations in the lab and live viewing of mask wearers outdoors. On the other hand, none of these studies has measured detection during active social interaction with the mask wearer (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies of hyper-realistic mask perception have assessed spontaneous detection of masks during an orthogonal task (social inference ratings; Sanders et al, 2017 ). Detection rates approached floor levels in that situation, precluding individual differences analysis.…”

Section: Methodsmentioning

confidence: 99%

“…Experimental evidence bears out this conclusion. In a series of studies (Sanders et al, 2017 ), we examined incidental detection of unexpected but attended hyper-realistic masks in both photographic and live presentations. In all of these studies, viewers accepted hyper-realistic masks as real faces.…”

Section: Introductionmentioning

confidence: 99%

“…Nightingale, Wade, & Watson, 2017 ), but the very success of computer graphics has raised awareness that on-screen images may be digitally generated or enhanced. One of the interesting aspects of hyper-realistic masks is that they also fool the eye in the physical world (Sanders et al, 2017 ), where digital image manipulation has not yet encroached.

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Section: Introductionmentioning

confidence: 99%

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Individual differences in hyper-realistic mask detection

Sanders

Jenkins

2018

Cogn. Research

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Hyper-realistic masks present a new challenge to security and crime prevention. We have recently shown that people’s ability to differentiate these masks from real faces is extremely limited. Here we consider individual differences as a means to improve mask detection. Participants categorized single images as masks or real faces in a computer-based task. Experiment 1 revealed poor accuracy (40%) and large individual differences (5–100%) for high-realism masks among low-realism masks and real faces. Individual differences in mask categorization accuracy remained large when the Low-realism condition was eliminated (Experiment 2). Accuracy for mask images was not correlated with accuracy for real face images or with prior knowledge of hyper-realistic face masks. Image analysis revealed that mask and face stimuli were most strongly differentiated in the region below the eyes. Moreover, high-performing participants tracked the differential information in this area, but low-performing participants did not. Like other face tasks (e.g. identification), hyper-realistic mask detection gives rise to large individual differences in performance. Unlike many other face tasks, performance may be localized to a specific image cue.

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The super‐recogniser advantage extends to the detection of hyper‐realistic face masks

Robertson,

Davis,

Sanders

et al. 2024

Applied Cognitive Psychology

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Hyper‐realistic silicone masks provide a viable route to identity fraud. Over the last decade, more than 40 known criminal acts have been committed by perpetrators using this type of disguise. With the increasing availability and bespoke sophistication of these masks, research must now focus on ways to enhance their detection. In this study, we investigate whether super‐recognisers (SRs), people who excel at identity recognition, are more likely to detect this type of fraud, in comparison to typical‐recogniser controls. Across three tasks, we examined mask detection rates in the absence of a pre‐task prompt (covert task), and again after making participants aware of their use in criminal settings (explicit task). Finally, participants were asked to indicate which aspects of the masks could support their detection (regions of interest task). The findings show an SR advantage for the detection of hyper‐realistic masks across the covert and explicit mask detection tasks. In addition, the eye, mouth, and nose regions appear to be particularly indicative of the presence of a mask. The lack of natural skin texture, proportional features, expressiveness, and asymmetry are also salient cues. The theoretical and applied implications of these findings are discussed.

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Hyper-realistic face masks: a new challenge in person identification

Cited by 16 publications

References 42 publications

Detecting morphed passport photos: a training and individual differences approach

Detecting morphed passport photos: a training and individual differences approach

Individual differences in hyper-realistic mask detection

The super‐recogniser advantage extends to the detection of hyper‐realistic face masks

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