With more people wearing masks to prevent the spread of the coronavirus, facial recognition developers have accelerated their efforts in adapting their technology to recognize individual faces (including iris identification) through facial coverings.
According to GCN:
In April, researchers posted an image dataset to GitHub featuring 1,200 Instagram selfies. They tagged the images to identify people wearing medical masks, non-medical masks or no mask to support innovative solutions for clothed facial recognition issues.
Based on the masked face dataset, developers will be able to build face detection and recognition algorithms to help identify people wearing masks traveling in and out of communities or facilities that require identity verification. Additionally, they said, facial security checks at train stations and other checkpoints can be upgraded to detect pedestrians wearing masks. Based on the constructed datasets, the eye-focused masked face recognition model they designed and trained has an identification accuracy over 95%, they said.
Japan’s NEC, which provides the facial-recognition technology used by Customs and Border Patrol at U.S. airports, has launched new rounds of testing now that masks are more common. According to Benji Hutchinson, a vice president with NEC’s U.S. division, the company’s algorithms have always tested on face masks because they are commonly worn in Asia during flu seasons. “Masks are nothing new to us, but that doesn’t mean it’s all perfect,” Hutchinson told Wired. He said NEC is advising customers, such as CBP, to make their own decisions about the technology for now.
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