User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse




Conference Paper

Emergent Leadership Detection Across Datasets


Müller,  Philipp
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

External Ressource
No external resources are shared
Fulltext (public)

(Preprint), 2MB

Supplementary Material (public)
There is no public supplementary material available

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.

Cite as: http://hdl.handle.net/21.11116/0000-0003-8F7F-F
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.