24 October 2016 Measuring cues for stand-off deception detection based on full-body nonverbal features in body-worn cameras
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Abstract
Deception detection is valuable in the security domain to distinguish truth from lies. It is desirable in many security applications, such as suspect and witness interviews and airport passenger screening. Interviewers are constantly trying to assess the credibility of a statement, usually based on intuition without objective technical support. However, psychological research has shown that humans can hardly perform better than random guessing. Deception detection is a multi-disciplinary research area with an interest from different fields, such as psychology and computer science. In the last decade, several developments have helped to improve the accuracy of lie detection (e.g., with a concealed information test, increasing the cognitive load, or measurements with motion capture suits) and relevant cues have been discovered (e.g., eye blinking or fiddling with the fingers). With an increasing presence of mobile phones and bodycams in society, a mobile, stand-off, automatic deception detection methodology based on various cues from the whole body would create new application opportunities. In this paper, we study the feasibility of measuring these visual cues automatically on different parts of the body, laying the groundwork for stand-off deception detection in more flexible and mobile deployable sensors, such as body-worn cameras. We give an extensive overview of recent developments in two communities: in the behavioral-science community the developments that improve deception detection with a special attention to the observed relevant non-verbal cues, and in the computer-vision community the recent methods that are able to measure these cues. The cues are extracted from several body parts: the eyes, the mouth, the head and the fullbody pose. We performed an experiment using several state-of-the-art video-content-analysis (VCA) techniques to assess the quality of robustly measuring these visual cues.
Conference Presentation
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Henri Bouma, Henri Bouma, Gertjan Burghouts, Gertjan Burghouts, Richard den Hollander, Richard den Hollander, Sophie van der Zee, Sophie van der Zee, Jan Baan, Jan Baan, Johan-Martijn ten Hove, Johan-Martijn ten Hove, Sjaak van Diepen, Sjaak van Diepen, Paul van den Haak, Paul van den Haak, Jeroen van Rest, Jeroen van Rest, "Measuring cues for stand-off deception detection based on full-body nonverbal features in body-worn cameras", Proc. SPIE 9995, Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XII, 99950N (24 October 2016); doi: 10.1117/12.2241183; https://doi.org/10.1117/12.2241183
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