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By Bryan Gardiner~WIRED
You can take off that ninja mask now. A new facial-recognition algorithm created by researchers at the University of California at Berkeley and University of Illinois at Urbana-Champaign is able to recognize faces with 90-95 percent accuracy, even if the eyes, nose and mouth are obscured.
“Most algorithms use what’s known as meaningful facial features to recognize people — things like the eyes, nose and mouth,” says Allen Yang, a postdoctoral researcher at UC Berkeley’s College of Engineering who developed the new algorithm. “But that’s incredibly limiting because you’re only looking at pixels from a designated portion of the face and those pixels end up being much smaller than the whole image. Our algorithm shows that you only need to randomly select pixels from anywhere on the face. If you select enough of them, you can produce extremely high accuracy.”
Yang’s new algorithm, which was created with the help of a team of researchers at UIUC, could mark a quantum leap in face-recognition technology. Current feature-based systems have accuracy that tops out at 65 percent when some form of occlusion is introduced. They also require relatively high-resolution images, and can easily be fooled by changing small details such as adding a mustache, donning a hood or changing one’s expression.
The secret sauce in Yang’s new method is a mathematical technique for solving linear equations with sparse entries called, appropriately enough, sparse representation (.pdf). While all other facial-recognition algorithms tend to compare a given feature set against all others in a database (generating percentages of likeliness along the way), Yang’s algorithm ignores all but the most compelling match from one subject — basically, its most confident choice.
