# Emergent Leadership Detection Across Datasets

arXiv: https://arxiv.org/abs/1905.02058 · HTML: https://mlsanity.com/paper/1905.02058/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2019.

## Built on by (most cited first, 5 of 5)

- [Bodily Behaviors in Social Interaction: Novel Annotations and State-of-the-Art Evaluation](https://mlsanity.com/paper/2207.12817/) (2022)
- [Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey](https://mlsanity.com/paper/2207.10574/) (2022)
- [Gaze-enhanced Crossmodal Embeddings for Emotion Recognition](https://mlsanity.com/paper/2205.00129/) (2022)
- [Multimodal Vision Transformers with Forced Attention for Behavior Analysis](https://mlsanity.com/paper/2212.03968/) (2022)
- [MultiMediate'23: Engagement Estimation and Bodily Behaviour Recognition in Social Interactions](https://mlsanity.com/paper/2308.08256/) (2023)

## Abstract (arXiv)

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.