U.S. agency: Pandemic masks thwarting face recognition tech

Having a tough time recognizing your neighbors behind their pandemic masks? Computers are finding it more difficult, too.

A preliminary study published by a U.S. agency on Monday found that even the best commercial facial recognition systems have error rates as high as 50% when trying to identify masked faces.

The mask problem is why Apple earlier this year made it easier for iPhone owners to unlock their phones without Face ID. It could also be thwarting attempts by authorities to identify individual people at Black Lives Matter protests and other gatherings.

The National Institute of Standards and Technology says it is launching an investigation to better understand how facial recognition performs on covered faces. Its preliminary study examined only those algorithms created before the pandemic, but its next step is to look at how accuracy could improve as commercial providers adapt their technology to an era when so many people are wearing masks.

Some companies, including those that work with law enforcement, have tried to tailor their face-scanning algorithms to focus on people’s eyes and eyebrows.

NIST, which is a part of the Commerce Department, is working with the U.S. Customs and Border Protection and the Department of Homeland Security's science office to study the problem.

It tested the software by drawing digital masks onto the faces in a trove of border crossing photographs, and then compared those photos against another database of unmasked people seeking visas and other immigration benefits. The agency says it scanned 6.2 million images of about 1 million people using 89 algorithms supplied by tech firms and academic labs.

Under ideal conditions, NIST says the failure rate for the best facial recognition systems is only about 0.3%, though research has...