# Emergent Leadership Detection Across Datasets

## TL;DR

This paper evaluates the generalization of emergent leadership detection methods across different datasets, highlighting the potential of pose and eye contact features for real-world applications.

## Contribution

It is the first to perform cross-dataset evaluation for emergent leadership detection, assessing robustness and generalization of current methods.

## Key findings

- Cross-dataset prediction accuracy of 0.68 using pose and eye contact features.
- Evaluation of robustness of visual focus, body pose, facial actions, and speaking activity.
- Demonstrates potential for real-world emergent leadership detection.

## 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.