Credit: B. Hayes, M. Ngan/NIST
This illustration shows some of the digitally applied face mask variations used in the NIST study, including the four different colors used (black, red, white and light blue) and some of the different shapes and amounts of face covering.
A new study of face recognition technology created after the onset of the COVID-19 pandemic shows that some software developers have made demonstrable progress at recognizing masked faces.
The findings, produced by the National Institute of Standards and Technology (NIST), are detailed in a new report called Ongoing Face Recognition Vendor Test (FRVT) Part 6B: Face Recognition Accuracy with Face Masks Using Post-COVID-19 Algorithms (NISTIR 8331). It is the agency’s first study that measures the performance of face recognition algorithms developed following the arrival of the pandemic. A previous report from July explored the effect of masked faces on algorithms submitted before March 2020, indicating that software available before the pandemic often had more trouble with masked faces.
“Some newer algorithms from developers performed significantly better than their predecessors. In some cases, error rates decreased by as much as a factor of 10 between their pre- and post-COVID algorithms,” said NIST’s Mei Ngan, one of the study’s authors. “In the best cases, software algorithms are making errors between 2.4 and 5% of the time on masked faces, comparable to where the technology was in 2017 on nonmasked photos.”
The new study adds the performance of 65 newly submitted algorithms to those that were tested on masked faces in the previous round, offering cumulative results for 152 total al ..
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