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Who is that stranger in your social media photo? A click on the face reveals the name in seconds, almost as soon as you can identify your best friend. While that handy app is not quite ready for your smart phone, researchers are racing to develop reliable methods to match one person’s photo from millions of images for a variety of applications. The National Institute of Standards and Technology (NIST) reports that results from its 2013 test of facial recognition algorithms show that accuracy has improved up to 30 percent since 2010.
According to HomeLnad Security New Wire a NIST release reports that the report by NIST biometric researchers Patrick Grother and Mei Ngan, Performance of Face identification Algorithms, includes results from algorithms submitted by sixteen organizations.
Researchers defined performance by recognition accuracy — how many times the software correctly identified the photo — and the time the algorithms took to match one photo against massive photo data sets.
“We studied the one-to-many identification because it is the largest market for face recognition technology,” Grother said. “These algorithms are used around the world to detect duplicates in databases, fraudulent applications for passports and driving licenses, in token-less access control, surveillance, social media tagging, lookalike discovery and criminal investigations.”
Four research groups enrolled in both the 2013 and the previous 2010 test, allowing NIST researchers to compare performance improvements over time. They found that those groups had improved their performance on the tests by from 10 and almost 30 percent. One organization decreased its error rate from 8.9 percent in 2010 to 6.4 percent in 2013.
In both years the study used a database of 1.6 million faces. In 2010, the images were frontal “mugshot” images from law enforcement agencies