Tuesday, December 1, 2020

Face Recognition Software Shows Improvement in Recognizing Masked Faces

 

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Face Recognition Software Shows Improvement in Recognizing Masked Faces

Eight face photos with artificial digital coverings in the shape of masks show variations used in the NIST study.

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.

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A woman's face appears six times, each time wearing a different digitally applied mask shape.NIST Launches Studies Into Masks’ Effect on Face Recognition Software

July 27, 2020
Researchers found that algorithms created before the pandemic generally perform less accurately with digitally masked faces.
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