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Background Deception is present in all walks of life, from social interactions to matters of homeland security. Nevertheless, reliable indicators of deceptive behavior in real‐life 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., What classifies lying from truth‐telling behavior? The current study adopts a physiological‐social approach, combining state‐of‐the‐art techniques from electrical and neuro‐engineering (nanotechnology‐based skin printed electrodes for measuring electromyography), computer science (machine‐learning algorithms), and cognitive psychology (deception task) to automatically and objectively characterize different deceptive behaviors based on “giveaway” signs of facial expressions.