A computer system that reads visual cues on the human face has been developed by researchers.
Inspired by the work of psychologists who study the human face for clues that someone is telling a high-stakes lie, computer scientists including Ifeoma Nwogu, Nisha Bhaskaran and Venu Govindaraju 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 analysed 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, whose full name is pronounced "e-fo-ma nwo-gu."
In the past, UB communication professor Mark G. Frank, a behavioral scientist whose primary area of research has been facial expressions and deception, has attempted to automate deceit detection using systems that analyze changes in body heat or examine a slew of involuntary facial expressions.
The automated UB system tracked a different trait - eye movement. The system employed a statistical technique to model how people moved their eyes in two distinct situations: during regular conversation, and while fielding a question designed to prompt a lie.
People whose pattern of eye movements changed between the first and second scenario were assumed to be lying, while those who maintained consistent eye movement were assumed to be telling the truth.
Previous experiments in which human judges coded facial movements found documentable differences in eye contact at times when subjects told a high-stakes lie.
What Nwogu and fellow computer scientists did was create an automated system that could verify and improve upon information used by human coders to successfully classify liars and truth tellers.
The next step will be to expand the number of 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 behaviour 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 colours, 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.
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.