Deception Detection through Automatic, Unobtrusive Analysis of Nonverbal Behavior
IEEE Intelligent Systems
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Motion, like speech, provides information about one's emotional state. This work introduces an automated non-verbal audio-visual approach for detecting deceptive roles in multi-party conversations using low resolution video. We show how using simple features extracted from motion and speech improves over speech-only for the detection of deceptive roles. Our results show that deceptive players were recognised with significantly higher precision when video features were used. We improve the classification performance with 22.6% compared to our baseline.