Study Evaluates Effects of Race, Age, Sex on Face Recognition Software
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A new NIST study examines how accurately face recognition software tools identify people of varied sex, age and racial background. According to the findings of the study 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.
Tests showed a wide range in accuracy across developers, with the most accurate algorithms producing many fewer errors. The researcher team saw higher rates of false positives for Asian and African American faces relative to images of Caucasians. The differentials often ranged from a factor of 10 to 100 times, depending on the individual algorithm.
The National Institute of Standards and Technology (NIST) is part of the U.S. Department of Commerce.
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