Multimodal people ID for a multimedia meeting browser
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
A Surveillance System Combining Peripheral and Foveated Motion Tracking
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Tracking Focus of Attention in Meetings
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Face Cataloger: Multi-Scale Imaging for Relating Identity to Location
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Automatic Analysis of Multimodal Group Actions in Meetings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-View Head Pose Estimation using Neural Networks
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
A GENERIC FACE REPRESENTATION APPROACH FOR LOCAL APPEARANCE BASED FACE VERIFICATION
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Communicative gestures in coreference identification in multiparty meetings
Proceedings of the 2009 international conference on Multimodal interfaces
Efficient person identification using active cameras in a smartroom
Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis
Multi-sensor fusion through adaptive bayesian networks
AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
Enhancing biometric recognition with spatio-temporal reasoning in smart environments
Personal and Ubiquitous Computing
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The automatic detection, tracking, and identification of multiple people in intelligent environments are important building blocks on which smart interaction systems can be designed. Those could be, e.g., gesture recognizers, head pose estimators or far-field speech recognizers and dialog systems. In this paper, we present a system which is capable of tracking multiple people in a smart room environment while inferring their identities in a completely automatic and unobtrusive way. It relies on a set of fixed and active cameras to track the users and get close-ups of their faces for identification, and on several microphone arrays to determine active speakers and steer the attention of the system. Information coming asynchronously from several sources, such as position updates from audio or visual trackers and identification events from identification modules, is fused at higher level to gradually refine the room's situation model. The system has been trained on a small set of users and showed good performance at acquiring and keeping their identities in a smart room environment.