ICMI '05 Proceedings of the 7th international conference on Multimodal interfaces
Proceedings of the 9th international conference on Multimodal interfaces
Fast and Robust Face Tracking for Analyzing Multiparty Face-to-Face Meetings
MLMI '08 Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
ICMI '08 Proceedings of the 10th international conference on Multimodal interfaces
Real-time Visual Tracker by Stream Processing
Journal of Signal Processing Systems
Automatic nonverbal analysis of social interaction in small groups: A review
Image and Vision Computing
A speaker diarization method based on the probabilistic fusion of audio-visual location information
Proceedings of the 2009 international conference on Multimodal interfaces
Conversation scene analysis based on dynamic Bayesian network and image-based gaze detection
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Metacommunication and semiotic engineering: insights from a study with mediated HCI
DUXU'13 Proceedings of the Second international conference on Design, User Experience, and Usability: design philosophy, methods, and tools - Volume Part I
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This presentation overviews our recent progress in multimodal conversation scene analysis, and discusses its future in terms of designing better human-to-human communication systems. Conversation scene analysis aims to provide the automatic description of conversation scenes from the multimodal nonverbal behaviors of participants as captured by cameras and microphones. So far, the author's group has proposed a research framework based on the probabilistic modeling of conversation phenomena for solving several basic problems including speaker diarization, i.e. "who is speaking when", addressee identification, i.e. "who is talking to whom", interaction structure, i.e. "who is responding to whom", the estimation of visual focus of attention (VFOA), i.e. "who is looking at whom", and the inference of interpersonal emotion such as "who has empathy/antipathy with whom", from observed multimodal behaviors including utterances, head pose, head gestures, eye-gaze, and facial expressions. This paper overviews our approach and discusses how conversation scene analysis can be extended to enhance the design process of computer-mediated communication systems.