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

Abstract

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., how frequently 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, Neuroticism), 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.