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Tuesday, July 27, 2021

Detecting Deception with Machine Leaning

Quite interesting, but my guess, if something like this sees much use, will be heavily regulated.  Below intro, more at the link. 

Detecting Deception

By Sandrine Ceurstemont,   Commissioned by CACM Staff  in CACM

 People are not good at detecting when someone is lying. Studies have shown that our ability to perceive deception is barely greater than chance.  Wasiq Khan, a senior lecturer in artificial intelligence and data sciences at Liverpool John Moores University in the U.K., thinks that is partly because it requires the ability to identify complex clues in speech, facial movements, and gestures, attributes that he says "cannot be observed by humans easily."

Automated systems that use machine learning may be able to do better. Khan and his colleagues developed such a system while working on a project for the EU, where the aim was to explore new technologies that could be put in place to improve border control. They examined whether deception could be detected automatically from eye and facial cues, such as blink rate and gaze aversion. "I wanted to investigate whether face movements or eye movements are important," says Khan.

The team recorded videos of 100 participants to use as their dataset. The volunteers were filmed while role-playing a scenario that might occur at a nation's port of entry, in which they are asked about what they had packed in their suitcase by an avatar controlled by the researchers in another room. Half of the participants were asked to lie, and the other half were told to be truthful.   

The videos were then analyzed using an automated system called Silent Talker. It examined each video frame and used an algorithm to extract information from the interviewees about 36 face and eye movements. Results were noted in binary format where 1 could be assigned when the person's eyes were closed, for example, and 0 if they were open.  The team them tried to determine which facial and eye features were correlated with deception by using various clustering algorithms. "The video analysis is complex," says Khan.

The algorithms identified features that seemed to be most important for detecting deception, which all involved tiny eye movements. The team then trained three machine learning algorithms using both the more significant features and the total set of attributes. Eighty percent of the dataset was used for training, including 40 truthful and 40 deceitful interviews, while the remaining 20% was held for testing.

Khan and his colleagues found the machine learning methods were all able to predict deception quite well from the identified features. Overall accuracy ranged from 72% to 78% depending on the method, where the greatest accuracy was obtained by focusing solely on eye movements. "We identified that eye features are important and contain significant clues for deception," says Khan.  ... ' 

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