Software Shows Improvement in Recognizing Masked Faces
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.
The Black Emergency Managers Association International
BLACK FIRE BRIGADE
African Public Health Coalition
Upward African Women
Mission is to increase the diversity of corporate America by increasing the diversity of business school faculty. We attract African-Americans, Hispanic-Americans and Native Americans to business Ph.D. programs, and provide a network of peer support on their journey to becoming professors.