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Human nonverbal emotional communication in dyadic dialogs is a process of mutual influence and adaptation. Identifying the direction of influence, or cause-effect relation between participants, is a challenging task due to two main obstacles. First, distinct emotions might not be clearly visible. Second, participants causeeffect relation is transient and variant over time. In this paper, we address these difficulties by using facial expressions that can be present even when strong distinct facial emotions are not visible. We also propose to apply a relevant interval selection approach prior to causal inference to identify those transient intervals where adaptation process occurs. To identify the direction of influence, we apply the concept of Granger causality to the time series of facial expressions on the set of relevant intervals. We tested our approach on synthetic data and then applied it to newly, experimentally obtained data. Here, we were able to show that a more sensitive facial expression detection algorithm and a relevant interval detection approach is most promising to reveal the cause-effect pattern for dyadic communication in various instructed interaction conditions. arXiv:1810.12171v1 [cs.CV] 29 Oct 2018 4. We propose a method for the selection of the relevant time intervals where GC should be applied, and show based on synthetic as well as real data, the superiority of the proposed method in detecting cause-effect relations when compared to applying GC on the full time series.

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