Computer can spot false faces better than you!

A computer has beaten humans when it comes to identify real from fake facial expressions of pain.

Researchers at University of California (UC) San Diego and University of Toronto have found that a computer system spots real or fake expressions of pain more accurately than people can.

"The computer system managed to detect distinctive dynamic features of facial expressions that people missed," said Marian Bartlett, a research professor at UC San Diego's institute for neural computation.

"Humans can simulate facial expressions and fake emotions well enough to deceive most observers. The computer's pattern-recognition abilities prove better at telling whether pain is real or faked," added Kang Lee, professor at the Eric Jackman institute of child study at University of Toronto.

The research team found that humans could not discriminate real from fake expressions of pain better than random chance - and, even after training, only improved accuracy to a modest 55 percent.

The computer system attains an 85 percent accuracy.

According to Lee, in highly social species such as humans, faces have evolved to convey rich information - including expressions of emotion and pain.

Because of the way our brains are built, people can simulate emotions they are 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, he noted.

The new approach has the potential to elucidate 'behavioral fingerprints' of the neural-control systems involved in emotional signalling.

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.

The computer-vision system might be used to detect other real-world deceptive actions in the realms of homeland security, psychopathology, job screening, medicine and law, Bartlett said.

The work, titled 'Automatic Decoding of Deceptive Pain Expressions' was published in the journal Current Biology.