ICMI 2016

18th ACM International Conference on Multimodal Interaction (ICMI 2016), November 12–16, 2016, Tokyo, Japan

Desktop Layout

Poster Session 2
Main Track
Conference Room 3
Detecting Emergent Leader in a Meeting Environment using Nonverbal Visual Features Only
Cigdem Beyan, Nicolò Carissimi, Francesca Capozzi, Sebastiano Vascon, Matteo Bustreo, Antonio Pierro, Cristina Becchio, and Vittorio Murino
(IIT Genoa, Italy; McGill University, Canada; University of Venice, Italy; Sapienza University of Rome, Italy; University of Turin, Italy)
Publisher's Version
Abstract: In this paper, we propose an effective method for emergent leader detection in meeting environments which is based on nonverbal visual features. Identifying emergent leader is an important issue for organizations. It is also a well-investigated topic in social psychology while a relatively new problem in social signal processing (SSP). The effectiveness of nonverbal features have been shown by many previous SSP studies. In general, the nonverbal video-based features were not more effective compared to audio-based features although, their fusion generally improved the overall performance. However, in absence of audio sensors, the accurate detection of social interactions is still crucial. Motivating from that, we propose novel, automatically extracted, nonverbal features to identify the emergent leadership. The extracted nonverbal features were based on automatically estimated visual focus of attention which is based on head pose. The evaluation of the proposed method and the defined features were realized using a new dataset which is firstly introduced in this paper including its design, collection and annotation. The effectiveness of the features and the method were also compared with many state of the art features and methods.


Time stamp: 2019-12-13T06:02:47+01:00