The Journal of Machine Learning Research
Multimodal recognition of personality traits in social interactions
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
Information and Software Technology
Automatic nonverbal analysis of social interaction in small groups: A review
Image and Vision Computing
Automatic prediction of individual performance from "thin slices" of social behavior
MM '09 Proceedings of the 17th ACM international conference on Multimedia
IEEE Transactions on Multimedia
Learning large margin likelihoods for realtime head pose tracking
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Employing social gaze and speaking activity for automatic determination of the Extraversion trait
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Mining Group Nonverbal Conversational Patterns Using Probabilistic Topic Models
IEEE Transactions on Multimedia
A semi-automated system for accurate gaze coding in natural dyadic interactions
Proceedings of the 15th ACM on International conference on multimodal interaction
Context-based conversational hand gesture classification in narrative interaction
Proceedings of the 15th ACM on International conference on multimodal interaction
3D head pose and gaze tracking and their application to diverse multimodal tasks
Proceedings of the 15th ACM on International conference on multimodal interaction
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This paper addresses the task of mining typical behavioral patterns from small group face-to-face interactions and linking them to social-psychological group variables. Towards this goal, we define group speaking and looking cues by aggregating automatically extracted cues at the individual and dyadic levels. Then, we define a bag of nonverbal patterns (Bag-of-NVPs) to discretize the group cues. The topics learnt using the Latent Dirichlet Allocation (LDA) topic model are then interpreted by studying the correlations with group variables such as group composition, group interpersonal perception, and group performance. Our results show that both group behavior cues and topics have significant correlations with (and predictive information for) all the above variables. For our study, we use interactions with unacquainted members i.e. newly formed groups.