Detecting leadership while understanding the underlying behavior is an important research topic particularly for social and organizational psychology, and has started to get attention from social signal processing research community as well. It is known that, visual activity is a useful cue to investigate the social interactions, even though previously applied nonverbal features based on head/body actions were not performing well enough for identification of emergent leaders (ELs) in small group meetings. Starting from these premises, in this study, we propose an effective method that uses 2D body pose based nonverbal features to represent the visual activity of a person. Our results suggest that, i) overall, the proposed nonverbal features derived from body pose perform better than existing visual activity based features, ii) it is possible to improve classification results by applying unsupervised feature learning as a preprocessing step, and iii) the proposed nonverbal features are able to advance the EL identification performances of other types of nonverbal features when they are used together.

Moving as a leader: Detecting emergent leadership in small groups using body pose / Beyan, C.; Katsageorgiou, V. -M.; Murino, V.. - (2017), pp. 1425-1433. (Intervento presentato al convegno 25th ACM International Conference on Multimedia, MM 2017 tenutosi a Mountain View, California, USA nel 2017) [10.1145/3123266.3123404].

Moving as a leader: Detecting emergent leadership in small groups using body pose

Beyan C.;
2017-01-01

Abstract

Detecting leadership while understanding the underlying behavior is an important research topic particularly for social and organizational psychology, and has started to get attention from social signal processing research community as well. It is known that, visual activity is a useful cue to investigate the social interactions, even though previously applied nonverbal features based on head/body actions were not performing well enough for identification of emergent leaders (ELs) in small group meetings. Starting from these premises, in this study, we propose an effective method that uses 2D body pose based nonverbal features to represent the visual activity of a person. Our results suggest that, i) overall, the proposed nonverbal features derived from body pose perform better than existing visual activity based features, ii) it is possible to improve classification results by applying unsupervised feature learning as a preprocessing step, and iii) the proposed nonverbal features are able to advance the EL identification performances of other types of nonverbal features when they are used together.
2017
MM 2017 - Proceedings of the 2017 ACM Multimedia Conference
1515 BROADWAY, NEW YORK, NY 10036-9998 USA
Association for Computing Machinery, Inc
9781450349062
Beyan, C.; Katsageorgiou, V. -M.; Murino, V.
Moving as a leader: Detecting emergent leadership in small groups using body pose / Beyan, C.; Katsageorgiou, V. -M.; Murino, V.. - (2017), pp. 1425-1433. (Intervento presentato al convegno 25th ACM International Conference on Multimedia, MM 2017 tenutosi a Mountain View, California, USA nel 2017) [10.1145/3123266.3123404].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/298065
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