PayEye dual biometrics win FIDO certification with zero PAD errors
Polish fintech provider PayEye has obtained its FIDO Biometric Component Certification and proven highly resistant to presentation attacks, according to a news release.
PayEye submitted the latest version of its eyePOS payment system, equipped with the company’s proprietary iris recognition algorithm, to trials run by private U.S. lab iBeta. EyePOS uses a fusion of face and iris biometrics to authenticate the identity of the person making a payment.
The FIDO Alliance certification requires evaluation of both biometric matching performance and spoof attack detection.
In tests to measure presentation attack detection, PayEye attained an error rate of 0 percent, meaning its eyePOS payment terminals’ biometric detection capability rejected fourteen different types of A and B level FIDO attacks over 300 attempts.
“This certification is an important step and a milestone for our company, as it proves that our technology is accurate and secured against attacks,” says PayEye CEO Daniel Jarząb. Calling PayEye “a system that you know how to use the moment you see it,” Jarząb touted the company’s unique combination of face and iris biometrics.
“No biometrics-based payments vendor before us has integrated two complementary biometric characteristics in such a natural and intuitive way,” he said. “We chose iris and face for the ultimate user experience, as these traits are widely available, contactless, and hygienic. We believe that this is the future of payments.”
Speaking in October 2022 at the Trust Services Forum/CA Day in Berlin, Germany, iProov CEO Andrew Bud identified presentation attacks as a major threat to consider in implementing automated biometric verification for digital identity, along with fraudulent identity claims in person, and digital injection attacks such as deepfakes.
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Article Topics
biometric payments | biometric testing | biometrics | certification | face biometrics | FIDO Alliance | iBeta | iris biometrics | PayEye | POS | presentation attack detection
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