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Rapport, the close and harmonious relationship in which interaction partnersare "in sync" with each other, was shown to result in smoother socialinteractions, improved collaboration, and improved interpersonal outcomes. Inthis work, we are first to investigate automatic prediction of low rapportduring natural interactions within small groups. This task is challenging giventhat rapport only manifests in subtle non-verbal signals that are, in addition,subject to influences of group dynamics as well as inter-personalidiosyncrasies. We record videos of unscripted discussions of three to fourpeople using a multi-view camera system and microphones. We analyse a rich setof non-verbal signals for rapport detection, namely facial expressions, handmotion, gaze, speaker turns, and speech prosody. Using facial features, we candetect low rapport with an average precision of 0.7 (chance level at 0.25),while incorporating prior knowledge of participants' personalities can evenachieve early prediction without a drop in performance. We further provide adetailed analysis of different feature sets and the amount of informationcontained in different temporal segments of the interactions.

Detecting Low Rapport During Natural Interactions in Small Groups from Non-Verbal Behaviour