Researchers from the US National Institute of Standards and Technology found that face masks are causing facial recognition algorithms to fail as much as 50% of the time.
In a report, the US National Institute of Standards and Technology found that face masks were thwarting even the most advanced facial recognition algorithms. Error rates varied from 5% to 50%, depending on an algorithm’s capabilities.
The results are troubling for the facial recognition industry which has been scrambling to develop algorithms that can identify people through their eyes and nose alone as people turn to face masks amid the coronavirus pandemic.
The masks have caused trouble for facial recognition software prompting tech companies to adapt.
Apple has pushed an update so face ID can work even when people are wearing covers.
Facial recognition algorithms rely on getting as many data points on a person’s image as possible. Masks tend to take away a lot of valuable identifying information.
The study found that algorithms are already finicky enough that improper lighting or a bad angle can fool the technology — and masks make matters worse.
One algorithm that would have an error rate of 0.3% surged to a 5% when presented with images of people wearing masks. The study tested the effectiveness of 89 facial recognition algorithms against face masks.
The test looked at the algorithms one-to-one matching capabilities which is essentially comparing one photo of a person to a different picture but with a mask on. NIST used six million images for its research and applied masks digitally with different variations of the coverings.
The study also found that the more of the nose that was covered the more likely the mask was to foil the algorithms. Black masks were also more likely to fool the algorithms than blue ones.
NIST said this was the first of a series of tests surrounding facial recognition and face masks.
Mei Ngan, a NIST researcher said: “With the arrival of the pandemic we need to understand how face recognition technology deals with masked faces. We have begun by focusing on how an algorithm developed before the pandemic might be affected by subjects wearing face masks.”