Weighted geodesic flow kernel for interpersonal mutual influence modeling and emotion recognition in dyadic interactions
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.