Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset

Cristina Palmero, Javier Selva, Sorina Smeureanu, Julio C. S. Jacques Junior, Albert Clapes, Alexa Mosegui, Zejian Zhang, David Gallardo, Georgina Guilera, David Leiva, Sergio Escalera; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2021, pp. 1-12

Abstract


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.

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[bibtex]
@InProceedings{Palmero_2021_WACV, author = {Palmero, Cristina and Selva, Javier and Smeureanu, Sorina and Junior, Julio C. S. Jacques and Clapes, Albert and Mosegui, Alexa and Zhang, Zejian and Gallardo, David and Guilera, Georgina and Leiva, David and Escalera, Sergio}, title = {Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops}, month = {January}, year = {2021}, pages = {1-12} }