Tom Simonite of the MIT Technology Review reports, "Asked whether two unfamiliar photos of faces show the same person, a human being will get it right 97.53 percent of the time. New software developed by researchers at Facebook can score 97.25 percent on the same challenge, regardless of variations in lighting or whether the person in the picture is directly facing the camera. That’s a significant advance over previous face-matching software, and it demonstrates the power of a new approach to artificial intelligence known as deep learning, which Facebook and its competitors have bet heavily on in the past year."
Simonite continues, "This area of AI involves software that uses networks of simulated neurons to learn to recognize patterns in large amounts of data. 'You normally don’t see that sort of improvement,' says Yaniv Taigman, a member of Facebook’s AI team, a research group created last year to explore how deep learning might help the company (see 'Facebook Launches Advanced AI Effort'). 'We closely approach human performance,' says Taigman of the new software. He notes that the error rate has been reduced by more than a quarter relative to earlier software that can take on the same task. Facebook’s new software, known as DeepFace, performs what researchers call facial verification (it recognizes that two images show the same face), not facial recognition (putting a name to a face). But some of the underlying techniques could be applied to that problem, says Taigman, and might therefore improve Facebook’s accuracy at suggesting whom users should tag in a newly uploaded photo."
Image: Courtesy Facebook