Computers may be able to spot real or faked expressions of pain more accurately than people can, according to a recent study.

Researchers from the University of California, San Diego and the University of Toronto found that a computer system could decode faked expressions of pain better than people. Humans could only discriminate real from faked expressions of pain 55 percent of the time.  55 percent, a computer system attains 85 percent accuracy.

"The computer system managed to detect distinctive dynamic features of facial expressions that people missed," Marian Bartlett, research professor at UC San Diego's Institute for Neural Computation and lead author of the study, said in a statement. "Human observers just aren't very good at telling real from faked expressions of pain."

Senior author Kang Lee, professor at the Dr. Eric Jackman Institute of Child Study at the University of Toronto, added that the computer's pattern-recognition abilities prove better at telling whether pain is real or faked.

"In highly social species such as humans, faces have evolved to convey rich information, including expressions of emotion and pain," Lee said. "And, because of the way our brains are built, people can simulate emotions they're not actually experiencing -- so successfully that they fool other people. The computer is much better at spotting the subtle differences between involuntary and voluntary facial movements."

"By revealing the dynamics of facial action through machine vision systems," said Bartlett, "our approach has the potential to elucidate 'behavioral fingerprints' of the neural-control systems involved in emotional signaling."

The single most predictive feature of falsified expressions, the study shows, is the mouth, and how and when it opens. Fakers' mouths open with less variation and too regularly.

"Further investigations," said the researchers, "will explore whether over-regularity is a general feature of fake expressions."

In addition to detecting pain malingering, the computer-vision system could be used to detect other real-world deceptive actions in the realms of homeland security, psychopathology, job screening, medicine, and law, Bartlett said.