Researchers evaluate facial recognition techniques

A research team led by Alice O’Toole, a professor in The University of Texas at Dallas School of Behavioral and Brain Sciences, is evaluating how well the rapidly evolving generation of facial-recognition software is performing. The researchers are comparing the rates of success for the software to the rates for non-technological, but presumably expert human evaluation.

“The government is interested in spotting people who might pose a danger,” O’Toole said. “But they also don’t want to have too many false alarms and detain people who are not real risks.” The studies in the Face Perception and Research Laboratories are funded by the US Department of Defense.

O’Toole and her team are carefully examining where the algorithms succeed and where they come up short. They’re using point-by-point comparisons to examine similarities in millions of faces captured within a database, and then comparing results to algorithm determinations. In the studies, humans and algorithms decided whether pairs of face images, taken under different illumination conditions, were pictures of the same person or different people.

So far, the results of man vs. machine have been a bit surprising, O’Toole said. “In fact, the very best algorithms performed better than humans at identifying faces,” she said. “Because most security applications rely primarily on human comparisons up until now, the results are encouraging about the prospect of using face recognition software in important environments.”

The researchers also are interested in the role race plays in humans’ ability to spot similar facial features. O’Toole said many studies indicate individuals almost always recognize similarities among members of their own race with more accuracy. But there is little research evaluating how technological tools differ in recognizing faces of varying races.

In a paper to be published in ACM Transactions on Applied Perception, O’Toole reports that the “other race effect” occurs for algorithms tested in a recent international competition for state-of-the-art face recognition algorithms. The study involved a Western algorithm made by fusing eight algorithms from Western countries and an East Asian algorithm made by fusing five algorithms from East Asian countries. At the low false-accept rates required for most security applications, the Western algorithm recognized Caucasian faces more accurately than East Asian faces, and the East Asian algorithm recognized East Asian faces more accurately than Caucasian faces.

The companies that develop the most reliable facial recognition software are likely to reap big profits down the line. Although governments may be their most obvious clients, there is also a great deal of interest from other major industries, such as casinos.

SOURCE: University of Texas at Dallas School of Behavioral and Brain Sciences

Posted by Conard Holton
Vision Systems Design