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IBIA breaks down biometrics misconceptions with NIST analysis

IBIA breaks down biometrics misconceptions with NIST analysis

The International Biometrics + Identity Association (IBIA) has addressed persistent myths about facial recognition, analyzing and presenting in clear language findings from research by the National Institute of Standards and Technology, in a new white paper. The document is part of a series of papers offering guidance and information for understanding the ethical and privacy implications of biometrics.

The IBIA’s white paper titled ‘Data Analysis of Facial Recognition Technology For a Diverse Population’ is accompanied by documents on ‘Facial Recognition Use Cases’, ‘Ethical Use of Biometric Technology’, and ‘Privacy Policy Principles’. The paper on use cases distinguishes “verification” from “identification,” the ethical use paper lists five criteria drawn from the organization’s 2019 ‘Principles for Biometric Data Security and Privacy’, and eight privacy policy principles are explained in the latter.

The organization notes Pew Research indicating that three-quarters of the American public knows little if anything about facial recognition, partly because of a lack of accessible, quality information. This has contributed to narratives based on conflated use cases and heightened concerns, IBIA says.

The analysis of facial recognition and diverse populations explains how detection error tradeoff (DET) curves are read and what they mean, providing examples of DET curves drawn from NIST testing for face biometric algorithms from IBIA members Cogent (Thales), Cognitec, Idemia and NEC, which show accuracy for Black people somewhere between negligibly higher and negligibly lower than it is for white people.

“The IBIA and our member companies are committed to the ethical use of our technologies, especially in the area of facial recognition, without racial or other biases,” comments IBIA Chairperson and Leidos Tech Fellow and Vice President of Homeland Security Solutions John Mears. “Our mission is to educate and advocate for best practices, policies and laws that balance civil rights and liberties with the need to properly identify individuals. As the NIST paper and our analysis make clear, facial recognition technologies can help eliminate the biases inherent to human review. Questions of bias are ones we take seriously, both in the data our technologies provide and how that data is applied by those using the technology. We look forward to the opportunity to engage more broadly on these important topics and facilitate a thoughtful dialogue on how we can all do better.”

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