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Although the coronavirus pandemic has escalated concerns around the evasion of facial
recognition systems, leaked US documents show these discussions taking place back in
2018 and 2019, too.
And while the debate on the use and legality of
facial recognition continues, the focus has
recently shifted to the challenges presented by mask-wearing in public.
On this front, the US National Institute of Standards and Technology (NIST) coordinated
a major research project to evaluate how masks impacted
the performance of various
facial recognition systems used across the globe.
Its report, published in July, found some algorithms struggled to correctly identify mask-
wearing individuals up to 50% of the time. This was a significant
error rate compared to
when the same algorithms analysed unmasked faces.
Some algorithms even struggled to locate a face when a mask was covering too much of it.
Finding ways around the problem
There are currently no usable photo data sets of mask-wearing people that can be used to
train and evaluate facial recognition systems.
The NIST study addressed this problem by superimposing masks (of
various colours, sizes
and positions) over images of faces, as seen here:
While this may not be a realistic portrayal of a person wearing a mask, it’s
effective
enough to study the effects of mask-wearing on facial recognition systems.
It’s possible images of real masked people would allow more details to be extracted to
improve recognition systems – perhaps by estimating the nose’s position based on visible
protrusions in the mask.
Many facial recognition technology vendors are already preparing
for a future where mask
use will continue, or even increase. One US company offers masks with customers’ faces
printed on them, so they can unlock their smartphones without having to remove it.
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