The coordination of a leader with group members is very important for an effective leadership given that this figure is the person who actually manages the team members to achieve a desired goal. Investigating the leadership and especially the leadership style is a prominent research topic in social and organizational psychology. However this is a new problem in social signal processing that can actually make valuable contributions by analyzing multimodal data in a more effective and efficient way. In this work we identify the leadership style of an emergent leader (i.e. the leader who naturally arises from a group not designated) as autocratic or democratic. The proposed method is applied to a dataset in-the-wild; in other words there is no role-playing which is novel for this problem. Multiple kernel learning (MKL) using multimodal nonverbal features is utilized to predict leadership styles that proved to achieve better predictions as compared to traditional learning methods. Thanks to MKL and a simple heuristic proposed the best performing features are also identified showing that better predictions can be reached only by using those features. Additionally correlation analysis between the extracted nonverbal features and the results of social psychology questionnaire is also performed. This shows that significantly high correlations exist for speaking activity based and prosodic nonverbal features.

In social psychology, the leadership investigation is performed using questionnaires which are either i) self-administered or ii) applied to group participants to evaluate other members or iii) filled by external observers. While each of these sources is informative, using them individually might not be as effective as using them jointly. This paper is the first attempt which addresses the automatic identification of leaders in small-group meetings, by learning effective models using nonverbal audio-visual features and the results of social psychology questionnaires that reflect assessments regarding leadership. Learning is based on Multi-Task Learning which is performed without using ground-truth data (GT), but using the results of questionnaires (having substantial agreement with GT), administered to external observers and the participants of the meetings, as tasks. The results show that joint learning results in better performance as compared to single task learning and other baselines.

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.