Multiparty Visual Co-Occurrences for Estimating Personality Traits in Group Meetings

Participants’ body language during interactions with others in a group meeting can reveal important information about their individual personalities, as well as their contribution to a team. Here, we focus on the automatic extraction of visual features from each person, including her/his facial activity, body movement, and hand position, and how these features co-occur among team members (e.g., howfre- quently a person moves her/his arms or makes eye contact when she/he is the focus of attention of the group). We correlate these features with user questionnaires to reveal relationships with the "Big Five" personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroti- cism), as well as with team judgements about the leader and dominant contributor in a conversation. We demonstrate that our algorithms achieve state-of-the-art accuracy with an average of 80% for Big-Five personality trait prediction, potentially enabling integration into automatic group meeting understanding systems.