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

Abstract

We present a data-driven deep neural algorithm for detecting deceptive walking behavior using nonverbal cues like gaits and gestures. We conducted an elaborate user study, where we recorded many participants performing tasks involving deceptive walking. We extract the participants' walking gaits as series of 3D poses. We annotate various gestures performed by participants during their tasks. Based on the gait and gesture data, we train an LSTM-based deep neural network to obtain deep features. Finally, we use a combination of psychology-based gait, gesture, and deep features to detect deceptive walking with an accuracy of 93.4%. This is an improvement of 16.1% over handcrafted gait and gesture features and an improvement of 5.9% and 10.1% over classifiers based on the state-of-the-art emotion and action classification algorithms, respectively. Additionally, we present a novel dataset, DeceptiveWalk, that contains gaits and gestures with their associated deception labels. To the best of our knowledge, ours is the first algorithm to detect deceptive behavior using non-verbal cues of gait and gesture.

Deception Detection in Group Video Conversations using Dynamic Interaction Networks

“…Face-to-face deception. There has been much research on predicting whether an individual is deceptive from facial and body cues (Ding et al 2019;Randhavane et al 2019;Wang et al 2020) with extensions that also include audio and linguistic cues (Gogate, Adeel, and Hussain 2017;Wu et al 2018) However, most of these techniques do not work on group deception and do not consider inter-personal interactions in order to predict deception. We are the first to do so and to show that the Negative Dynamic Interaction Networks we propose are highly effective at detecting deception.…”

ProxEmo: Gait-based Emotion Learning and Multi-view Proxemic Fusion for Socially-Aware Robot Navigation

“…Ruiz-Garcia et al [29] and Tarnowski et al [30], use deep learning to classify different categories of emotion from facial expressions. Randhavane et al [31], [32] classify emotions into four classes based on affective features obtained from 3D skeletal poses extracted from human gait cycles. Their algorithm, however, requires a large number of 3D skeletal key-points to detect emotions and is limited to single individual cases.…”

EWareNet: Emotion Aware Human Intent Prediction and Adaptive Spatial Profile Fusion for Social Robot Navigation

“…[23] use multiple modalities such as facial cues, human pose and scene understanding. Randhavane et al [28,30] classify emotions into four classes based on affective features obtained from 3D skeletal poses extracted from human gait cycles. Their algorithm, however, requires a large number of 3D skeletal key-points to detect emotions and is limited to single individual cases.…”