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Affiliation

Department of Social Psychology, Friedrich Schiller University of Jena, Humboldtstrasse 26, 07743 Jena and Germany

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1 Papers

Causal Inference in Nonverbal Dyadic Communication with Relevant Interval Selection and Granger Causality

Topics: Face and Expression Recognition; Event and Human Activity Recognition

Authors: Lea Müller ¹ , Maha Shadaydeh ¹ , Martin Thümmel ¹ , Thomas Kessler ² , Dana Schneider ² and Joachim Denzler ³

Affiliations: ¹ Computer Vision Group, Friedrich Schiller University of Jena, Ernst-Abbe-Platz 2, 07743 Jena and Germany , ² Department of Social Psychology, Friedrich Schiller University of Jena, Humboldtstrasse 26, 07743 Jena and Germany , ³ Computer Vision Group, Friedrich Schiller University of Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany, Michael Stifel Center, Ernst-Abbe-Platz 2, 07743 Jena and Germany

Digital Object Identifier: 10.5220/0007399304900497

Proceedings of VISAPP 2019/VISIGRAPP, ISBN: 978-989-758-354-4

Quick Abstract

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 cause-effect 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.