Inspired by the work of psychologists who study the human face for clues that someone is telling a high-stakes lie, UB computer scientists are exploring whether machines can also read the visual cues that give away deceit.
Results so far are promising: In a study of 40 videotaped conversations, an automated system that analyzed eye movements correctly identified whether interview subjects were lying or telling the truth 82.5 percent of the time.
That’s a better accuracy rate than expert human interrogators typically achieve in lie-detection judgment experiments, said Ifeoma Nwogu, a research assistant professor at UB’s Center for Unified Biometrics and Sensors (CUBS) who helped develop the system.
In published results, even experienced interrogators average closer to 65 percent, Nwogu said.
“What we wanted to understand was whether there are signal changes emitted by people when they are lying, and can machines detect them?
The answer was yes, and yes,” said Nwogu.
In their study on automated deceit detection, Nwogu and her colleagues selected 40 videotaped interrogations.
They used the mundane beginning of each to establish what normal, baseline eye movement looked like for each subject, focusing on the rate of blinking and the frequency with which people shifted their direction of gaze.
The scientists then used their automated system to compare each subject’s baseline eye movements with eye movements during the critical section of each interrogation — the point at which interrogators stopped asking everyday questions and began inquiring about the check.
If the machine detected unusual variations from baseline eye movements at this time, the researchers predicted the subject was lying.