Identification of emergent leaders in a meeting scenario using multiple kernel learning

In this paper, an effective framework for detection of emergent leaders in small group is presented. In this scope, the combination of different types of nonverbal visual features; the visual focus of attention, head activity and body activity based features are utilized. Using them together ensued significant results. For the first time, multiple kernel learning (MKL) was applied for the identification of the most and the least emergent leaders. Taking the advantage of MKL's capability to use different kernels which corresponds to different feature subsets having different notions of similarity, significantly improved results compared to the state of the art methods were obtained. Additionally, high correlations between the majority of the features and the social psychology questionnaires which are designed to estimate the leadership or dominance were demonstrated.