A US government study has found that facial recognition technology is getting better at identifying people wearing masks. The study is part of an ongoing research by the US National Institute of Standards and Technology (NIST). The agency has examined the effectiveness of more than 150 facial recognition systems on people wearing face coverings. The systems are powered by machine learning algorithms (算法).
The first results of the study were announced in July, as health officials across the world urged people to wear masks to limit the spread of COVID-19. New findings were released this week. Police agencies have long used facial recognition technology to search for and help catch criminals. It can also be used to unlock phones or other electronic devices. Some robots use facial recognition technology to recognize the people they are communicating with. However, the wide use of masks in public has created major difficulties for such systems.
The study looked at facial recognition systems already in use before the pandemic. It also looked at systems specially developed to work on masked faces. The NIST said it processed a total of 6. 2 million images for the experiment People in the images were not actually wearing masks. So, the researchers digitally added different mask shapes to faces in the pictures for use in the study. In some cases, up to 70 percent of a person's face was covered in the images. Overall, the research shows the top-performing facial recognition systems fail to correctly identify unmasked individuals about 0. 3 percent of the time and the failure rate rose to about 5 percent with masked images. Many of the lower performing algorithms, however, had much higher error rates with masked images — as high as 20 to 50 percent.
In the latest findings, researchers included results from 65 new facial recognition systems that have been developed since the start of the pandemic. "Some of these systems performed "significantly better" than the earlier ones," Mei Ngan, a lead researcher on the project, said in a statement. The study also found round-shaped masks — which cover only the mouth and nose — led to fewer errors than wider ones that stretch across the cheeks. The new study also ran tests to see whether different colored masks would affect error rates. The team used red, white, black and light blue. The research findings suggested that generally, the red and black masks led to higher failure rates than the other colors.
