A new study shows facial recognition software developed after the start of the COVID-19 pandemic is doing a better job at recognizing masked faces.
The report produced by the National Institute of Standards and Technology is the first to measure performance of face recognition algorithms developed after the onset of the pandemic. In earlier studies, software available before the pandemic often had trouble with masked faces.
“In the best cases, software algorithms are making errors between 2.4% and 5% of the time on masked faces, comparable to where the technology was in 2017, on unmasked faces,” said NIST’s Mei Ngan, one of the study’s authors.
The new study adds the performance of 65 newly submitted algorithms to those tested on masked faces in the previous round.
Using the same 6 million images it had used previously, researchers again tested the algorithms’ ability to perform “one-to-one” matching, in which a photo is compared with a different photo of the same person,
“It’s the function used for unlocking our cellphones, to trying to authenticate our identities when we’re using e-gates at airports,” Ngan told WTOP.
As with the earlier testing, the images had mask shapes digitally applied, rather than showing people wearing actual masks.
Some of the NIST report’s finding include:
When both the new image and the stored image are of masked faces, error