Multiparty Visual Co-Occurrences for Estimating Personality Traits in Group Meetings
Multiparty Visual Co-Occurrences for Estimating Personality Traits in Group Meetings
复制标题
DOI：
10.1109/wacv45572.2020.9093642
复制
发表时间：
2020-03
期刊：
影响因子：
--
通讯作者：
Lingyu Zhang;Indrani Bhattacharya;Mallory M. Morgan;Michael Foley;Christoph Riedl;B. F. Welles;R. Radke
中科院分区：
文献类型：
--
作者：
Lingyu Zhang;Indrani Bhattacharya;Mallory M. Morgan;Michael Foley;Christoph Riedl;B. F. Welles;R. Radke
在小组会议中，参与者在与他人互动时的肢体语言可以透露出关于他们个人个性的重要信息，以及他们对团队的贡献。在这里，我们专注于从每个人中自动提取视觉特征，包括她/他的面部活动、身体运动和手部位置，以及这些特征如何在团队成员中共同出现(例如，当她/他是团队关注的焦点时，一个人移动她/他的手臂或进行眼神交流的频率)。我们将这些特征与用户问卷相关联，以揭示与“五大”人格特质(开放性、尽责性、外向性、宜人性、神经质)的关系，以及与团队对对话中领导者和主要贡献者的判断的关系。我们证明，我们的算法达到了最先进的准确性，平均达到80%的五大人格特质预测，潜在地能够集成到自动小组会议理解系统中。
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.