New NIST Study Evaluates Effects of Race, Age, Sex on Face Recognition Software

From NIST (via

How accurately do face recognition software tools identify people of varied sex, age and racial background? According to a new study by the National Institute of Standards and Technology (NIST), the answer depends on the algorithm at the heart of the system, the application that uses it and the data it’s fed — but the majority of face recognition algorithms exhibit demographic differentials. A differential means that an algorithm’s ability to match two images of the same person varies from one demographic group to another.

Results captured in the report, NISTIR 8280 Face Recognition Vendor Test (FRVT) Part 3: Demographic Effects, are intended to inform policymakers and to help software developers better understand the performance of their algorithms. Face recognition technology has inspired public debate in part because of the need to understand the effect of demographics on face recognition algorithms.

“While it is usually incorrect to make statements across algorithms, we found empirical evidence for the existence of demographic differentials in the majority of the face recognition algorithms we studied,” said Patrick Grother, a NIST computer scientist and the report’s primary author. “While we do not explore what might cause these differentials, this data will be valuable to policymakers, developers and end users in thinking about the limitations and appropriate use of these algorithms.”

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