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Found 8 papers, 4 papers with code
DISCOVER: A Data-driven Interactive System for Comprehensive Observation, Visualization, and ExploRation of Human Behaviour
To address these barriers, we introduce DISCOVER -- a modular and flexible, yet user-friendly software framework specifically developed to streamline computational-driven data exploration for human behavior analysis.
REACT 2024: the Second Multiple Appropriate Facial Reaction Generation Challenge
In dyadic interactions, humans communicate their intentions and state of mind using verbal and non-verbal cues, where multiple different facial reactions might be appropriate in response to a specific speaker behaviour.
MultiMediate'23: Engagement Estimation and Bodily Behaviour Recognition in Social Interactions
This paper describes the MultiMediate'23 challenge and presents novel sets of annotations for both tasks.
REACT2023: the first Multi-modal Multiple Appropriate Facial Reaction Generation Challenge
The Multi-modal Multiple Appropriate Facial Reaction Generation Challenge (REACT2023) is the first competition event focused on evaluating multimedia processing and machine learning techniques for generating human-appropriate facial reactions in various dyadic interaction scenarios, with all participants competing strictly under the same conditions.
ForDigitStress: A multi-modal stress dataset employing a digital job interview scenario
We present a multi-modal stress dataset that uses digital job interviews to induce stress.
"GAN I hire you?" -- A System for Personalized Virtual Job Interview Training
This feedback can be helpful concerning the improvement of behavioral skills needed for job interviews.
Applying Cooperative Machine Learning to Speed Up the Annotation of Social Signals in Large Multi-modal Corpora
Scientific disciplines, such as Behavioural Psychology, Anthropology and recently Social Signal Processing are concerned with the systematic exploration of human behaviour.
Interpreting social cues to generate credible affective reactions of virtual job interviewers
In this paper we describe a mechanism of generating credible affective reactions in a virtual recruiter during an interaction with a user.
