Mass surveillance has taken a hit
Face masks have become a daily part of our lives in this COVID-19 situation and they are one of the best defenses against the spread of the virus. What else, is it doing good ?
Well, Face masks are breaking face recognition algorithms and governments are not liking it
A new study by the US National Institute of Standards and Technology (NIST) found that wearing face masks which covers the mouth and nose areas has caused the error rate of some of the most widely used facial recognition algorithms to spike between 5 percent and 50 percent.
Black masks were more likely to cause errors than blue masks, and the more of the nose area is covered by the mask, the harder the algorithms found it to identify the face.
Mei Ngan, an author of the report and NIST computer scientist stated that,
“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. Later this summer, we plan to test the accuracy of algorithms that were intentionally developed with masked faces in mind.”
The facial recognition algorithm tests conducts by NIST measures the distance between features in a target’s face. Masks reduce the accuracy of these algorithms by hiding most of the facial features, although some still remain visible to the system.
NIST’s report only tested a type of facial recognition known as one-to-one matching. This is the same 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 totally different to the 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.
THE DEPARTMENT OF HOMELAND SECURITY IS UPSET WITH THESE ISSUES CAUSED BY FACE MASKS
Although NIST’s report doesn’t cover one-to-many systems, these are generally considered more error pone than one-to-one algorithms. Picking out faces in a crowd is harder because it is complex to get the proper angle, lighting and the resolution is generally reduced. That suggest that if face masks are breaking one-to-one systems, they’re likely breaking one-to-many algorithms at a greater frequency.
Earlier this year, an internal bulletin from the US Department of Homeland Security reported by The Intercept, said the agency was concerned about the “potential impacts that widespread use of protective masks could have on security operations that incorporate face recognition systems.”
Anyway this is not the end to this problem.
Tech companies have been rapidly adapting to this new world, designing algorithms that identify faces just using the area around the eyes with the help of Machine Learning and Artificial Intelligence.
NIST also believes that the facial technology will improve in the future and they are planning to test specially tuned facial recognition algorithms for mask wearers later this year which will include the testing of one-to-many facial recognition system.
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