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
Copyright Status
Unknown
Socpus ID
85047348920 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85047348920
STARS Citation
Yang, Zhaojun; Gong, Boqing; and Narayanan, Shrikanth, "Weighted Geodesic Flow Kernel For Interpersonal Mutual Influence Modeling And Emotion Recognition In Dyadic Interactions" (2018). Scopus Export 2015-2019. 9524.
https://stars.library.ucf.edu/scopus2015/9524