Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Ongoing Face Recognition Vendor Test (FRVT) Part 6B: Face recognition accuracy with face masks using post-COVID-19 algorithms

Published

Author(s)

Mei L. Ngan, Patrick J. Grother, Kayee K. Hanaoka

Abstract

This is the second of a series of reports on the performance of face recognition algorithms on faces occluded by protective face masks commonly worn to reduce inhalation and exhalation of viruses. This is a continuous study is being run under the Ongoing Face Recognition Vendor Test (FRVT) executed by the National Institute of Standards and Technology (NIST). In our first report, we tested "pre-pandemic" algorithms that were already submitted to FRVT 1:1 prior to mid-March 2020. This report augments its predecessor with results for more recent algorithms provided to NIST after the COVID-19 pandemic was declared. While we do not have information on whether or not a particular algorithm was designed with face coverings in mind, the results show evidence that a number of developers have adapted their algorithms to support face recognition on subjects potentially wearing face masks. The algorithms tested were one- to-one algorithms submitted to the FRVT 1:1 Verification track. Future editions of this document will also report accuracy of one-to- many algorithms.
Citation
NIST Interagency/Internal Report (NISTIR) - 8331
Report Number
8331

Keywords

face masks, biometrics, face recognition

Citation

Ngan, M. , Grother, P. and Hanaoka, K. (2020), Ongoing Face Recognition Vendor Test (FRVT) Part 6B: Face recognition accuracy with face masks using post-COVID-19 algorithms, NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.IR.8331 (Accessed May 10, 2021)
Created November 29, 2020