NIST has published an update to NISTIR 8331 - Ongoing FRVT Part 6B: Face recognition accuracy with face masks using post-COVID-19 algorithms, aimed at quantifying face recognition accuracy for people wearing masks. This update adds 35 new algorithms submitted to FRVT 1:1 since the last report (and includes cumulative results for 198 algorithms evaluated to date).
Our initial approach has been to apply masks to faces digitally (i.e., using software to apply a synthetic mask). This allowed us to leverage large datasets that we already have. This report quantifies the effect of masks on both false negative and false positives match rates. For more information, visit the FRVT Face Mask Effects webpage.
A new FRVT report released as NISTIR 8280 - FRVT Part 3: Demographic Effects on December 19th, 2019, describes and quantifies demographic differentials for contemporary face recognition algorithms. NIST has conducted tests to quantify demographic differences for nearly 200 face recognition algorithms from nearly 100 developers, using four collections of photographs with more than 18 million images of more than 8 million people. Using both one-to-one verification and one-to-many identification algorithms submitted to NIST, the report found empirical evidence for the existence of a wide range of accuracy across demographic differences in the majority of the current face recognition algorithms that were evaluated.