Machine can tell when a human being is lying

subjects studied and develop automated systems that analyze body language in addition to eye contact.

Nwogu said that while the sample size was small, the findings are exciting.

They suggest that computers may be able to learn enough about a person’s behavior in a short time to assist with a task that challenges even experienced interrogators. The videos used in the study showed people with various skin colors, head poses, lighting and obstructions such as glasses.

This does not mean machines are ready to replace human questioners, however — only that computers can be a helpful tool in identifying liars, Nwogu said.

She noted that the technology is not foolproof: A very small percentage of subjects studied were excellent liars, maintaining their usual eye movement patterns as they lied. Also, the nature of an interrogation and interrogators’ expertise can influence the effectiveness of the lie-detection method.

The videos used in the study were culled from a set of 132 that Frank recorded during a previous experiment.

In Frank’s original study, 132 interview subjects were given the option to “steal” a check made out to a political party or cause they strongly opposed.

Subjects who took the check but lied about it successfully to a retired law enforcement interrogator received rewards for themselves and a group they supported; Subjects caught lying incurred a penalty: they and their group received no money, but the group they despised did.

Subjects who did not steal the check faced similar punishment if judged lying, but received a smaller sum for being judged truthful.

The interrogators opened each interview by posing basic, everyday questions. Following this mundane conversation, the interrogators asked about the check. At this critical point, the monetary rewards and penalties increased the stakes of lying, creating an incentive to deceive and do it well.

In their study on automated deceit detection, Nwogu and her colleagues selected forty 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.