This paper introduces UDIVA, a new non-acted dataset of face-to-face dyadic interactions, where interlocutors perform competitive and collaborative tasks with different behavior elicitation and cognitive workload. The dataset consists of 90.5 hours of dyadic interactions among 147 participants distributed in 188 sessions, recorded using multiple audiovisual and physiological sensors. Currently, it includes sociodemographic, self- and peer-reported personality, internal state, and relationship profiling from participants. As an initial analysis on UDIVA, we propose a transformer-based method for self-reported personality inference in dyadic scenarios, which uses audiovisual data and different sources of context from both interlocutors to regress a target person's personality traits. Preliminary results from an incremental study show consistent improvements when using all available context information.
Cite
@article{arxiv.2012.14259,
title = {Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset},
author = {Cristina Palmero and Javier Selva and Sorina Smeureanu and Julio C. S. Jacques Junior and Albert Clapés and Alexa Moseguí and Zejian Zhang and David Gallardo and Georgina Guilera and David Leiva and Sergio Escalera},
journal= {arXiv preprint arXiv:2012.14259},
year = {2020}
}
Comments
Accepted to the 11th International Workshop on Human Behavior Understanding workshop at Winter Conference on Applications of Computer Vision 2021