Improved Visual Focus of Attention Estimation and Prosodic Features for Analyzing Group Interactions

Collaborative group tasks require efficient and productive verbal and non-verbal interactions among the participants. Studying such interaction patterns could help groups perform more efficiently, but the detection and measurement of human behavior is challenging since it is inherently multi modal and changes on a millisecond time frame. In this paper, we present a method to study groups performing a collaborative decision-making task using non-verbal behavioral cues. First, we present a novel algorithm to estimate the visual focus of attention (VFOA) of participants using frontal cameras. The algorithm can be used in various group settings, and performs with a state-of-the-art accuracy of 90%. Secondly, we present prosodic features for non-verbal speech analysis. These features are commonly used in speech/music classification tasks, but are rarely used in human group interaction analysis. We validate our algorithms on a multimodal dataset of 14 group meetings with 45 participants, and show that a combination of VFOA-based visual metrics and prosodic feature-based metrics can predict emergent group leaders with 64% accuracy and dominant contributors with 86% accuracy. We also report our findings on the correlations between the non-verbal behavioral metrics with gender, emotional intelligence, and the Big 5 personality traits.