Facial recognition could now become even more widespread than before with companies marketing it as a contactless, potentially more hygienic, means of verifying identities in public places.

One firm that says its algorithms have continued to cope well during the pandemic is Swiss-based Tech5. Although the NIST report found that masks caused the error rate of firm’s system to jump from 0.45% to up to 14%, co-founder Rahul Parthe says the algorithm remained reliable enough in real-life settings.

“If somebody was wearing a mask and sunglasses and had a hat then you were basically missing a lot of their faces,” he explains. “That’s where we had to put in some requirements where a person would have to remove their hat or mask.”

Tech5’s systems are used in factories and schools in South East Asia, adds Mr Parthe, where they monitor daily attendance, for example.

And in schools, the system can perform an anonymous headcount, to see how many pupils were present on a particular day.

In these cases, where Tech5’s algorithm is passively monitoring images from a CCTV camera in a corridor, for instance, Mr Parthe says mask-wearers are asked to avoid confusing the system by not wearing hats as well.

However, he argues that his firm was well-positioned to adapt to the rise of face masks because, having so many clients in Asia, Tech5’s database had already been trained using lots of images of people wearing masks or religious garments that obscure part of the face.

“We never fine-tuned our algorithms just for face recognition with masks (during the pandemic),” a company spokeswoman (Yulia Bibikova) says.

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