Sensing and Modeling Human Networks using the Sociometer
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Modeling Individual and Group Actions in Meetings: A Two-Layer HMM Framework
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 7 - Volume 07
Automatic Analysis of Multimodal Group Actions in Meetings
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICMI '05 Proceedings of the 7th international conference on Multimodal interfaces
ICMI '08 Proceedings of the 10th international conference on Multimodal interfaces
Investigating automatic dominance estimation in groups from visual attention and speaking activity
ICMI '08 Proceedings of the 10th international conference on Multimodal interfaces
Predicting the dominant clique in meetings through fusion of nonverbal cues
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Automatic nonverbal analysis of social interaction in small groups: A review
Image and Vision Computing
Proceedings of the 2009 international conference on Multimodal interfaces
Modeling dominance in group conversations using nonverbal activity cues
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on multimodal processing in speech-based interactions
Characterizing conversational group dynamics using nonverbal behaviour
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Analyzing the structure of the emergent division of labor in multiparty collaboration
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Towards measuring the quality of interaction: communication through telepresence robots
Proceedings of the Workshop on Performance Metrics for Intelligent Systems
Detection of division of labor in multiparty collaboration
HCI'13 Proceedings of the 15th international conference on Human Interface and the Management of Information: information and interaction for learning, culture, collaboration and business - Volume Part III
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A novel measure for automatically quantifying the amount of interpersonal influence present in face-to-face conversations is proposed based on the visual-attention patterns of the participants as inferred from video sequences. First, we focus on the gaze of the participants as an indicator of addressing / listening behavior and build a probabilistic conversation model for inferring the gaze directions and conversation structures like monologue and dialogue, from observed utterances and head directions measured with image-based head trackers. Next, based on the estimates, the amount of influence is defined based on the amount of attention paid to speakers in monologues and to persons with whom the participants interact with during the dialogues. Experiments confirm that the proposed measures reveal some aspects of interpersonal influence in conversations.