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  Emergent Leadership Detection Across Datasets

Müller, P., & Bulling, A. (2019). Emergent Leadership Detection Across Datasets. In W. Gao, H. M. L. Meng, M. Turk, S. R. Fussell, B. W. Schuller, Y. Song, et al. (Eds.), ICMI '19 (pp. 274-278). New York, NY: ACM. doi:10.1145/3340555.3353721.

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arXiv:1905.02058.pdf (Preprint), 2MB
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 Creators:
Müller, Philipp1, Author           
Bulling, Andreas2, Author           
Affiliations:
1Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society, ou_1116547              
2External Organizations, ou_persistent22              

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Free keywords: Computer Science, Human-Computer Interaction, cs.HC
 Abstract: Automatic detection of emergent leaders in small groups from nonverbal
behaviour is a growing research topic in social signal processing but existing
methods were evaluated on single datasets -- an unrealistic assumption for
real-world applications in which systems are required to also work in settings
unseen at training time. It therefore remains unclear whether current methods
for emergent leadership detection generalise to similar but new settings and to
which extent. To overcome this limitation, we are the first to study a
cross-dataset evaluation setting for the emergent leadership detection task. We
provide evaluations for within- and cross-dataset prediction using two current
datasets (PAVIS and MPIIGroupInteraction), as well as an investigation on the
robustness of commonly used feature channels (visual focus of attention, body
pose, facial action units, speaking activity) and online prediction in the
cross-dataset setting. Our evaluations show that using pose and eye contact
based features, cross-dataset prediction is possible with an accuracy of 0.68,
as such providing another important piece of the puzzle towards emergent
leadership detection in the real world.

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Language(s): eng - English
 Dates: 2019-05-0620192019
 Publication Status: Issued
 Pages: 5 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Mueller_ICMI2019
DOI: 10.1145/3340555.3353721
 Degree: -

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Title: International Conference on Multimodal Interaction
Place of Event: Suzhou, China
Start-/End Date: 2019-10-14 - 2019-10-18

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Title: ICMI '19
  Subtitle : Proceedings of the 2019 International Conference on Multimodal Interaction
  Abbreviation : ICMI 2019
Source Genre: Proceedings
 Creator(s):
Gao, Wen1, Editor
Meng, Helen Mei Ling1, Editor
Turk, Matthew1, Editor
Fussell, Susan R.1, Editor
Schuller, Björn W.1, Editor
Song, Yale1, Editor
Yu, Kai1, Editor
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: New York, NY : ACM
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 274 - 278 Identifier: ISBN: 978-1-4503-6860-5