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NIST Results Once Again Demonstrate SAFR's Consistency and Fairness Among Racial Groups - SAFR from RealNetworks Secure Accurate Facial Recognition

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WIRED recently highlighted unacceptable levels of bias in facial recognition in the article The Best Algorithms Struggle to Recognize Black Faces Equally. They cited the poor test scores of leading facial recognition vendors, as reported by the National Institute of Standards and Technology (NIST) in its July 2019 results. WIRED specifically called out Idemia but generalized their concerns. "The NIST test challenged algorithms to verify that two photos showed the same face, similar to how a border agent would check passports. At sensitivity settings where Idemia's algorithms falsely matched different white women's faces at a rate of one in 10,000, it falsely matched black women's faces about once in 1,000 -- 10 times more frequently. A one in 10,000 false match rate is often used to evaluate facial recognition systems."


Home - SAFR from RealNetworks Secure Accurate Facial Recognition

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SAFR is a highly accurate, AI-based facial recognition platform architected to economically scale at high performance with rapid processing to detect and match millions of faces in real time. Recognize every fan, VIP, employee, or guest and surface key insights for a better experience.