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General Intelligence
Glasses Equipped With Facial Recognition Are Coming
New York-based Vuzix is selling augmented reality headsets to identify suspects
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If police around the world start wearing AR glasses equipped with facial recognition, there’s a good chance they’ll be made by Vuzix.
The Rochester, New York-based company has been by far the most bullish on the technology, partnering with companies around the world, including the infamous Clearview AI, to integrate facial recognition algorithms into its headset computer.
The push started in 2019, when Vuzix announced that it was partnering with another tech company, NNTC, to bring facial recognition to its devices. The technology was pitched as a solution for police and security professionals, who could now identify blacklisted individuals in real time. Suddenly, facial recognition without infrastructure like CCTV cameras became possible.
Now, Vuzix seems dead set on bringing facial recognition to its AR glasses. In February, Gizmodo reported that Vuzix was working with Clearview AI to bring its billion-person facial recognition to Vuzix’s AR glasses. (Clearview said at the time that the app was just a prototype.)
Vuzix also recently announced that it was working with a company called TensorMark to bring facial recognition to the company’s headsets. Vuzix is pitching its product as a solution not just for security, but also border patrol, first responders, retail, hospitality, and banking.
Vuzix isn’t the only company in this space, either. Chinese tech company Rokid, which makes smart AR glasses, has trialed facial recognition algorithms, according to tests performed by the U.S. National Institute of Standards and Technology. Another Chinese company, LLVision, is also manufacturing glasses outfitted with facial recognition that look similar to the now-defunct Google Glass.
Facial recognition in an AR headset raises all the same issues as the technology when deployed in CCTV cameras, including privacy and accuracy. But the small form factor also begs new questions, like…