New York's bid to identify road-going terrorists with facial recognition isn't going very smoothly so far. The Wall Street Journal has obtained a Metropolitan Transportation Authority email showing that a 2018 technology test on New York City's Robert F. Kennedy Bridge not only failed, but failed spectacularly -- it couldn't detect a single face "within acceptable parameters." An MTA spokesperson said the pilot program would continue at RFK as well as other bridges and tunnels, but it's not an auspicious start. The problem may be inherent to the early state of facial recognition at these speeds. Oak Ridge National Laboratory achieved more than 80 percent accuracy in a study that spotted faces through windshields, but that was at low speed.
Photos of people's faces are routinely taken from websites to help develop face recognition algorithms, without the subjects' consent, a report by NBC reveals. The latest example: In January IBM released a data set of almost a million photos that had been scraped from photo-sharing website Flickr then annotated with information about details like skin tone. The company pitched this as part of efforts to reduce the (very real) problem of bias within face recognition. However, it didn't get consent from anyone to do this, and it's almost impossible to get the photos removed. Dirty secret: IBM is far from alone.
To demonstrate how easy it is to track people without their knowledge, we collected public images of people who worked near Bryant Park (available on their employers' websites, for the most part) and ran one day of footage through Amazon's commercial facial recognition service. Our system detected 2,750 faces from a nine-hour period (not necessarily unique people, since a person could be captured in multiple frames). It returned several possible identifications, including one frame matched to a head shot of Richard Madonna, a professor at the SUNY College of Optometry, with an 89 percent similarity score.
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Microsoft claims its facial recognition technology just got a little less awful. Earlier this year, a study by MIT researchers found that tools from IBM, Microsoft, and Chinese company Megvii could correctly identify light-skinned men with 99-percent accuracy. But it incorrectly identified darker-skinned women as often as one-third of the time. Now imagine a computer incorrectly flagging an image at an airport or in a police database, and you can see how dangerous those errors could be. Microsoft's software performed poorly in the study.