Weighted Geodesic Flow Kernel For Interpersonal Mutual Influence Modeling And Emotion Recognition In Dyadic Interactions

Abstract

Interpersonal mutual influence occurs naturally in social interactions through various behavioral aspects of spoken words, speech prosody, body gestures and so on. Such interpersonal behavior dynamic flow along an interaction is often modulated by the underlying emotional states. This work focuses on modeling how a participant in a dyadic interaction adapts his/her behavior to the multimodal behavior of the interlocutor, to express the emotions. We propose a weighted geodesic flow kernel (WGFK) to capture the complex interpersonal relationship in the expressive human interactions. In our framework, we parameterize the interaction between two partners using WGFK in a Grassmann manifold by fine-grained modeling of the varying contributions in the behavior subspaces of interaction partners. We verify the effectiveness of the WGFK-based interaction modeling in multimodal emotion recognition tasks drawn from dyadic interactions.

Publication Date

1-29-2018

Publication Title

2017 7th International Conference on Affective Computing and Intelligent Interaction, ACII 2017

Volume

2018-January

Number of Pages

236-241

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ACII.2017.8273606

Socpus ID

85047348920 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85047348920

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