Detecting emergent leader in a meeting environment using nonverbal visual features only

In this paper, we propose an eective 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 eectiveness of nonverbal features have been shown by many previous SSP studies. In general, the nonverbal video-based features were not more eective compared to audio-based features although, their fusion generally improved the overall perfor- mance. However, in absence of audio sensors, the accurate detection of social interactions is still crucial. Motivating from that, we propose novel, automatically extracted, non- verbal 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 de- ned features were realized using a new dataset which is rstly introduced in this paper including its design, collec- tion and annotation. The eectiveness of the features and the method were also compared with many state of the art features and methods.