Lie to my face: An electromyography approach to the study of deceptive behavior

Abstract

Background: Deception is present in all walks of life, from social interactions to matters of homeland security. Nevertheless, reliable indicators of deceptive behavior in reallife scenarios remain elusive.Methods: By integrating electrophysiological and communicative approaches, we demonstrate a new and objective detection approach to identify participant-specific indicators of deceptive behavior in an interactive scenario of a two-person deception task. We recorded participants' facial muscle activity using novel dry screen-printed electrode arrays and applied machine-learning algorithms to identify lies based on brief facial responses.Results: With an average accuracy of 73%, we identified two groups of participants:Those who revealed their lies by activating their cheek muscles and those who activated their eyebrows. We found that the participants lied more often with time, with some switching their telltale muscle groups. Moreover, while the automated classifier, reported here, outperformed untrained human detectors, their performance was correlated, suggesting reliance on shared features. Conclusions:Our findings demonstrate the feasibility of using wearable electrode arrays in detecting human lies in a social setting and set the stage for future research on individual differences in deception expression.

Similarities and disparities between visual analysis and high-resolution electromyography of facial expressions

“…The analysis we presented in this paper suggests that the AU-based approach has an inherently limited ability to reliably compare between different sessions and individuals. Reservations about the validity of CV in studying facial expressions are recognized both among computer vision specialists [31] and among investigators studying facial expressions from psychological and neurological perspectives [17,32]. Accordingly, better algorithms and data sets PLOS ONE continue to be developed [33].…”

Wearable facial electromyography: in the face of new opportunities

“…Indeed, combining the EMG signals with support vector machine classification and unsupervised peak-density clustering, we recently showed that deceptive behavior and expressions of subjects that face each other, i.e. involved in direct interaction instead of in an artificial laboratory setup, is different than in traditional setups [40].…”

­­Building a Second-Opinion Tool for Classical Polygraph

“…Unorthodox lie detection studies analyze video and audio 6 (including facial expressions 7,8 , pupil reaction 9, and delays between question and answer 10) , electromyography (EMG) 11 , electroencephalogram (EEG) 12 , magnetic resonance tomography (MRT) 13,14 , or writing pattern (keystroke dynamics) 15 in addition to or instead of classical polygraph data. Some of these studies even get a chance to pilot in the new elds, such as the iBorderCtrl lie detector pilot in EU airports 16,17 or the VeriPol deception detection pilot by Spanish police on written insurance claims 18,19 .…”