Face Masks Disrupt Facial Recognition Algorithms, NIST Study Finds

GAITHERSBURG, Md. — Facial recognition algorithms developed prior to the coronavirus pandemic struggle mightily to identify masked faces, according to a preliminary study by the National Institute of Standards and Technology (NIST).
Even the best of the 89 commercial facial recognition algorithms tested had error rates between 5% and 50% in matching digitally applied face masks with photos of the same person without a mask, according to NIST.
The study results were published July 27 as a NIST Interagency Report ( NISTIR 8311 ), the first in a planned series from NIST’s Face Recognition Vendor Test (FRVT) program on the performance of facial recognition algorithms on faces partially covered by protective masks.
“With the arrival of the pandemic, we need to understand how face recognition technology deals with masked faces,” says Mei Ngan, a NIST computer scientist and an author of the report. “We have begun by focusing on how an algorithm developed before the pandemic might be affected by subjects wearing face masks. Later this summer, we plan to test the accuracy of algorithms that were intentionally developed with masked faces in mind.”
The NIST team explored how well each of the algorithms was able to perform “one-to-one” matching, where a photo is compared with a different photo of the same person. The function is commonly used for verification such as unlocking a smartphone or checking a passport.

Related: How Computer Vision Can Transform the Ability to Protect Individuals and Businesses

The team tested the algorithms on a set of about six million photos used in previous FRVT studies. (The team did not test the algorithms’ ability to perform “one-to-many” matching, used to determine whether a person in a photo matches any in a database of known images).
The research team digitally applied mask shapes to the original photos and tested the algorithms’ performance. Because real-world masks differ, the team came up with nine mask variants, which included differences in shape, color and nose coverage. The digital masks were black or a light blue that is approximately the same color as a blue surgical mask.
The shapes included round masks that cover the nose and mouth and a larger type as wide as the wearer’s face. These wider masks had high, medium and low variants that covered the nose to different degrees. The team then compared the results to the performance of the algorithms on unmasked faces.
“We can draw a few broad conclusions from the results, but there are caveats,” Ngan said. “None of these algorithms were designed to handle face masks, and the masks we used are digital creations, not the real thing.”
Keeping in mind these limitations, Ngan said, the study provides a few general lessons when comparing the performance of the tested algorithms on masked faces versus unmasked ones, including:

Algorithm accuracy with masked...