The weird have turned pro, but the advantage of mask-wearing is the disruption of facial recognition software without having to use geometric pattern disruption makeup or wearing special glasses.
The Economist magazine continues to model the 2020 Electoral College outcome with Trump dropping another 1% this week.
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Face masks are one of the best defenses against the spread of COVID-19, but their growing adoption is having a second, unintended effect: breaking facial recognition algorithms.
Wearing face masks that adequately cover the mouth and nose causes the error rate of some of the most widely used facial recognition algorithms to spike to between 5 percent and 50 percent, a study by the US National Institute of Standards and Technology (NIST) has found. Black masks were more likely to cause errors than blue masks, and the more of the nose covered by the mask, the harder the algorithms found it to identify the face.
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Although there’s been plenty of anecdotal evidence about face masks thwarting facial recognition, the study from NIST is particularly definitive. NIST is the government agency tasked with assessing the accuracy of these algorithms (along with many other systems) for the federal government, and its rankings of different vendors is extremely influential.
Notably, NIST’s report only tested a type of facial recognition known as one-to-one matching. This is the procedure used in border crossings and passport control scenarios, where the algorithm checks to see if the target’s face matches their ID. This is different to the sort of facial recognition system used for mass surveillance, where a crowd is scanned to find matches with faces in a database. This is called a one-to-many system.
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