An initial study by the National Institute of Standards and Technology (NIST) in the US has shown an error rate as high as 50% in facial recognition for people wearing face masks.
The government body tested 89 algorithms and found them to be between 5% and 50% inaccurate when comparing a photo of someone with a digitally applied face mask and a picture of that person unmasked.
The most accurate algorithms in use at border crossing fail to authenticate about 0.3% of unmasked people, the NIST said in its findings.
The study’s research was carried out before the coronavirus pandemic made wearing face masks commonplace.
The just-released report is planned as the first in a series assessing different aspects of masks on the performance of facial recognition technology.
“Given algorithm-specific variation, it is incumbent upon the system owner to know their algorithm. While publicly available test data from NIST and elsewhere can inform owners, it will usually be informative to specifically measure accuracy of the operational algorithm on the operational image data collected with actual masks,” the report’s authors wrote.
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