Your fellows matter: Affect analysis across subjects in group videos
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Publication Date
2019Journal Title
Proceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019
Conference Name
2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019)
ISSN
2326-5396
ISBN
9781728100890
Publisher
IEEE
Type
Conference Object
This Version
AM
Metadata
Show full item recordCitation
Mou, W., Gunes, H., & Patras, I. (2019). Your fellows matter: Affect analysis across subjects in group videos. Proceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019 https://doi.org/10.1109/FG.2019.8756514
Abstract
Automatic affect analysis has become a well established
research area in the last two decades. Recent works have
started moving from individual to group scenarios. However,
little attention has been paid to investigating how individuals in
a group influence the affective states of each other. In this paper,
we propose a novel framework for cross-subjects affect analysis
in group videos. Specifically, we analyze the correlation of the
affect among group members and investigate the automatic
recognition of the affect of one subject using the behaviours
expressed by another subject in the same group. A set of
experiments are conducted using a recently collected database
aimed at affect analysis in group settings. Our results show
that (1) people in the same group do share more information
in terms of behaviours and emotions than people in different
groups; and (2) the affect of one subject in a group can be
better predicted using the expressive behaviours of another
subject within the same group than using that of a subject
from a different group. This work is of great importance for
affect recognition in group settings: when the information of
one subject is unavailable due to occlusion, head/body poses
etc., we can predict his/her affect by employing the expressive
behaviours of the other subject(s).
Sponsorship
European Unions Horizon 2020
Funder references
European Commission Horizon 2020 (H2020) Societal Challenges (826232)
Identifiers
External DOI: https://doi.org/10.1109/FG.2019.8756514
This record's URL: https://www.repository.cam.ac.uk/handle/1810/290131
Rights
Licence:
http://www.rioxx.net/licenses/all-rights-reserved
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