The most accurate facial-recognition algorithms failed to correctly match a picture of a person wearing a digitally added mask to a different photo of the person without one between 5% and 50% of the time, according to a new report from federal researchers
This is the key finding of a new report released Monday from federal researchers at the National Institute of Standards and Technology, or
has spread to most countries around the world, claiming over 308,000 lives and infecting more than 4.6 million people. When tasked with matching a picture of a person wearing a digitally added face mask to a different photo of the person without one, the most accurate facial-recognition algorithms failed to make a correct match between 5% and 50% of the time, according to the report.
found that the best of these algorithms -— which were submitted to the lab before mid-March — failed just 0.3% of the time when tested on these same sets of photos without the digital masks. With the digital masks on, however, error rates climbed to 5% among these same algorithms.An obvious shortcoming of the report is that
digitally applied mask shapes to photos and tested the performance of face recognition algorithms developed before COVID appeared. Because real-world masks differ, the team came up with variants that included differences in shape, color and nose coverage.
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