Deception Detection through Automatic, Unobtrusive Analysis of Nonverbal Behavior (original) (raw)
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Video-Based Deception Detection
Studies in Computational Intelligence, 2008
This chapter outlines an approach for automatically extracting behavioral indicators from video and explores the possibility of using those indicators to predict human-interpretable judgments of involvement, dominance, tenseness, and arousal. The team utilized twodimensional spatial inputs extracted from video to construct a set of discrete and inter-relational features. Then three predictive models were created using the extracted features as predictors and human-coded perceptions of involvement, tenseness, and arousal as the criterion. Through this research, the team explores the feasibility and validity of the approach and identifies how such an approach could contribute to the broader community.
Motion Profiles for Deception Detection Using Visual Cues
Lecture Notes in Computer Science, 2010
We propose a data-driven, unobtrusive and covert method for automatic deception detection in interrogation interviews from visual cues only. Using skin blob analysis together with Active Shape Modeling, we continuously track and analyze the motion of the hands and head as a subject is responding to interview questions, as well as their facial micro expressions, thus extracting motion profiles, which we aggregate over each interview response. Our novelty lies in the representation of the motion profile distribution for each response. In particular, we use a kernel density estimator with uniform bins in log feature space. This scheme allows the representation of relatively over-controlled and relatively agitated behaviors of interviewed subjects, thus aiding in the discrimination of truthful and deceptive responses.
Detecting Deceptive Behaviours through Facial Cues from Videos: A Systematic Review
Applied Sciences
Interest in detecting deceptive behaviours by various application fields, such as security systems, political debates, advanced intelligent user interfaces, etc., makes automatic deception detection an active research topic. This interest has stimulated the development of many deception-detection methods in the literature in recent years. This work systematically reviews the literature focused on facial cues of deception. The most relevant methods applied in the literature of the last decade have been surveyed and classified according to the main steps of the facial-deception-detection process (video pre-processing, facial feature extraction, and decision making). Moreover, datasets used for the evaluation and future research directions have also been analysed.
Detecting Deception from Emotional and Unemotional Cues
Journal of Nonverbal Behavior, 2008
Encoders were video recorded giving either truthful or deceptive descriptions of video footage designed to generate either emotional or unemotional responses. Decoders were asked to indicate the truthfulness of each item, what cues they used in making their judgements, and then to complete both the Micro Expression Training Tool (METT) and Subtle Expression Training Tool (SETT). Although overall performance on the deception detection task was no better than chance, performance for emotional lie detection was significantly above chance, while that for unemotional lie detection was significantly below chance. Emotional lie detection accuracy was also significantly positively correlated with reported use of facial expressions and with performance on the SETT, but not on the METT. The study highlights the importance of taking the type of lie into account when assessing skill in deception detection. Keywords Deception detection Á Emotion Á SETT Á METT Deception, whether through omission or direct falsification, is a fundamental part of human social interaction (DePaulo et al. 2003). Deception may refer to anything from trivial, socalled ''white lies'', to situations in which the consequences of detected deception are grave-especially those involving the law. Although many lies are uncovered due to physical evidence or to the presence of third-party information (Park et al. 2002), sometimes this may be insufficient or even non-existent. In such contexts, lie detectors (such as law enforcement agents) may be forced to rely on other cues, such as nonverbal behavior, as indicators of a statement's truth or falsehood.
HMM-Based Deception Recognition from Visual Cues
2005
Behavioral indicators of deception and behavioral state are extremely difficult for humans to analyze. This research effort attempts to leverage automated systems to augment humans in detecting deception by analyzing nonverbal behavior on video. By tracking faces and hands of an individual, it is anticipated that objective behavioral indicators of deception can be isolated, extracted and synthesized to create a more accurate means for detecting human deception. Blob analysis, a method for analyzing the movement of the head and hands based on the identification of skin color is presented. A proof-ofconcept study is presented that uses blob analysis to extract visual cues and events, throughout the examined videos. The integration of these cues is done using a hierarchical Hidden Markov Model to explore behavioral state identification in the detection of deception, mainly involving the detection of agitated and over-controlled behaviors.
IEEE transactions on cybernetics, 2015
Detecting deception in interpersonal dialog is challenging since deceivers take advantage of the give-and-take of interaction to adapt to any sign of skepticism in an interlocutor's verbal and nonverbal feedback. Human detection accuracy is poor, often with no better than chance performance. In this investigation, we consider whether automated methods can produce better results and if emphasizing the possible disruption in interactional synchrony can signal whether an interactant is truthful or deceptive. We propose a data-driven and unobtrusive framework using visual cues that consists of face tracking, head movement detection, facial expression recognition, and interactional synchrony estimation. Analysis were conducted on 242 video samples from an experiment in which deceivers and truth-tellers interacted with professional interviewers either face-to-face or through computer mediation. Results revealed that the framework is able to automatically track head movements and expre...
2012
We thank those who kindly volunteered to participate in the study. We also thank David McCormick for encouragement and support and Aldert Vrij, University of Portsmouth, for helpful discussions and advice. 2 Summary This paper presents the development of a computerised, non-invasive psychological profiling system, „Silent Talker‟, for the analysis of nonverbal behaviour. Nonverbal signals hold rich information about mental, behavioural and/or physical states. Previous attempts to extract individual signals and to classify an overall behaviour have been time-consuming, costly, biased, error-prone and complex. Silent Talker overcomes these problems by the use of Artificial Neural Networks. The testing and validation of the system was undertaken by detecting processes associated with “deception ” and “truth”. In a simulated theft scenario thirty-nine participants „stole‟ (or didn‟t) money, and were interviewed about its location. Silent Talker was able to detect different behaviour pat...
2021
There is growing interest in the use of automated psychological profiling systems, specifically applying machine learning to the field of deception detection. Several psychological studies and machine-based models have been reporting the use of eye interaction, gaze and facial movements as important clues to deception detection. However, the identification of very specific and distinctive features is still required. For the first time, we investigate the fine-grained level eyes and facial micro-movements to identify the distinctive features that provide significant clues for the automated deception detection. A real-time deception detection approach was developed utilizing advanced computer vision and machine learning approaches to model the non-verbal deceptive behavior. Artificial neural networks, random forests and support vector machines were selected as base models for the data on the total of 262,000 discrete measurements with 1,26,291 and 128,735 of deceptive and truthful ins...
Automated Deception Detection of Males and Females From Non-Verbal Facial Micro-Gestures
2020 International Joint Conference on Neural Networks (IJCNN)
Gender bias within Artificial intelligence driven systems is currently a hot topic and is one of a number of areas where the data used to train, validate and test machine learning algorithms is under more scrutiny than ever before. In this paper we investigate if there is a difference between the nonverbal cues to deception generated by males and females through the use of an automated deception detection system. The system uses hierarchical neural networks to extract 36 channels of non-verbal head and facial behaviors whilst male and female participants are engaged in either a deceptive or truthful roleplaying task. An Image Vector dataset, comprising of 86584 vectors, is collated which uses a fixed sliding window slot of 1 second to record deceptive or truthful slots. Experiments were conducted on three variants of the dataset, all males, all females and mixed in order to examine if the differences in cues generated by males and females lead to differences in the accuracies of machine learning algorithms which classify their behavior. Results showed differences in nonverbal cues between males and females, with both genders at a disadvantage when treated by classifiers trained on both genders rather than classifiers specifically trained for each gender. However, there was no striking disadvantageous effect beyond the influence of their relative frequency of occurrence in the dataset.
Multimodal Deception Detection
Proceedings of the 2015 ACM on Workshop on Multimodal Deception Detection - WMDD '15, 2015
This work proposes a new approach to deception detection, based on finding significant differences between liars and truth tellers through the analysis of their behavior, verbal and non-verbal. This is based on the combination of two factors: multimodal data collection, and t-pattern analysis. Multimodal approach has been acknowledged in literature about deception detection and on several studies concerning the understanding of any communicative phenomenon. We believe a methodology such as T-pattern analysis could be able to get the best advantages from an approach that combines data coming from multiple signaling systems. In fact, T-pattern analysis is a recent methodology for the analysis of behavior that unveil the complex structure at the basis of the organization of human behavior. For this work, we conducted an experimental study and analyzed data related to a single subject. Results showed how T-pattern analysis allowed to find differences between truth telling and lying. This work aims at making progress in the state of knowledge about deception detection, with the final goal to propose a useful tool for the improvement of public security and well-being.