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Masks Are RUINING Nanny State Facial ID

“Face masks … make it much harder for facial-recognition software to identify you.”

Americans begin to redefine PPE as ‘Privacy Protection Equipment’

CNN – Face masks are already known to stop the spread of coronavirus.

Apparently, they can also make it much harder for facial-recognition software to identify you, too.

This is the key finding of a new report released Monday from federal researchers at the National Institute of Standards and Technology, or NIST, which is a branch of the US Commerce Department whose functions include measuring the accuracy of facial-recognition algorithms that companies and researchers submit to the lab.

When tasked with matching a picture of a person wearing a digitally added face mask to a different photo of the person without one, the most accurate facial-recognition algorithms failed to make a correct match between 5% and 50% of the time, according to the report.

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Generally speaking, most of the algorithms tested had failure rates of between 20% and 50%, Mei Ngan, a computer scientist at NIST and an author of the report, told CNN Business.

The identification issues make sense, as facial-recognition systems typically work by comparing measurements between different facial features in one image to those in another. Blocking off part of the face means there is less information for the software to use to make a match.

It highlights a unique challenge the tech industry is already working to confront as the pandemic continues.

While the technology is controversial, with a number of companies recently rethinking providing this technology to law enforcement, it’s used in a range of products and services, from using your face to unlock your smartphone to passing through a security checkpoint.

For their report, the researchers created nine different black and light blue mask shapes to account for the ways mask shapes vary in the real world and used them to hide part of a person’s face in a photo.

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They then compared a digitally masked photo of each person with another, unmasked photo of the same person … Read more. 

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