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Joint Engagement Classification using Video Augmentation Techniques for Multi-person Human-robot Interaction
[article]
2022
arXiv
pre-print
Affect understanding capability is essential for social robots to autonomously interact with a group of users in an intuitive and reciprocal way. However, the challenge of multi-person affect understanding comes from not only the accurate perception of each user's affective state (e.g., engagement) but also the recognition of the affect interplay between the members (e.g., joint engagement) that presents as complex, but subtle, nonverbal exchanges between them. Here we present a novel hybrid
arXiv:2212.14128v1
fatcat:wgshdzufmjg53mykezdatmhfbi