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Emergent Leadership Detection Across Datasets

Philipp Müller, Andreas Bulling

Proc. ACM International Conference on Multimodal Interaction (ICMI), 2019.


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 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 real-world emergent leadership detection.



@inproceedings{mueller19_icmi, title = {Emergent Leadership Detection Across Datasets}, author = {M{\"{u}}ller, Philipp and Bulling, Andreas}, year = {2019}, booktitle = {Proc. ACM International Conference on Multimodal Interaction (ICMI)}, doi = {10.1145/3340555.3353721} }


This work was funded, in part, by a JST CREST research grant under Grant No.: JPMJCR14E1, Japan. We thank Cigdem Beyan for sharing her code for pose feature computation.