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We present a data-driven deep neural algorithm for detecting deceptivewalking behavior using nonverbal cues like gaits and gestures. We conducted anelaborate user study, where we recorded many participants performing tasksinvolving deceptive walking. We extract the participants' walking gaits asseries of 3D poses. We annotate various gestures performed by participantsduring their tasks. Based on the gait and gesture data, we train an LSTM-baseddeep neural network to obtain deep features. Finally, we use a combination ofpsychology-based gait, gesture, and deep features to detect deceptive walkingwith an accuracy of 88.41%. This is an improvement of 10.6% over handcraftedgait and gesture features and an improvement of 4.7% and 9.2% over classifiersbased on the state-of-the-art emotion and action classification algorithms,respectively. Additionally, we present a novel dataset, DeceptiveWalk, thatcontains gaits and gestures with their associated deception labels. To the bestof our knowledge, ours is the first algorithm to detect deceptive behaviorusing non-verbal cues of gait and gesture.

The Liar's Walk: Detecting Deception with Gait and Gesture