This paper addresses the problem of automatically predicting the dominant clique (i.e., the set of K-dominant people) in face-to-face small group meetings recorded by multiple audio and video sensors. For this goal, we present a framework that integrates automatically extracted nonverbal cues and dominance prediction models. Easily computable audio and visual activity cues are automatically extracted from cameras and microphones. Such nonverbal cues, correlated to human display and perception of dominance, are well documented in the social psychology literature. The effectiveness of the cues were systematically investigated as single cues as well as in unimodal and multimodal combinations using unsupervised and supervised learning approaches for dominant clique estimation. Our framework was evaluated on a five-hour public corpus of teamwork meetings with thirdparty manual annotation of perceived dominance. Our best approaches can exactly predict the dominant clique with 80.8% accuracy in four-person meetings in which multiple human annotators agree on their judgments of perceived dominance.
The task of predicting the dominant clique, employing a similar approach, was attempted in REF .
Predicting the dominant clique in meetings through fusion of nonverbal cues
ACM Multimedia
Computer Science