![]() It focuses on how well each individual algorithm performs one of two different tasks that are among face recognition’s most common applications. The NIST study evaluated 189 software algorithms from 99 developers - a majority of the industry. While NIST does not test the finalized commercial products that make use of these algorithms, the program has revealed rapid developments in the burgeoning field. The study was conducted through NIST’s Face Recognition Vendor Test (FRVT) program, which evaluates face recognition algorithms submitted by industry and academic developers on their ability to perform different tasks. “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.” “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. Face recognition technology has inspired public debate in part because of the need to understand the effect of demographics on face recognition algorithms. Results captured in the report, Face Recognition Vendor Test (FRVT) Part 3: Demographic Effects(NISTIR 8280), are intended to inform policymakers and to help software developers better understand the performance of their algorithms. A differential means that an algorithm’s ability to match two images of the same person varies from one demographic group to another. 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. ![]()
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