A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Parametrized structure from motion for 3D adaptive feedback tracking of faces
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
A model-based gaze tracking system
IJSIS '96 Proceedings of the 1996 IEEE International Joint Symposia on Intelligence and Systems
Recognition of human head orientation based on artificial neural networks
IEEE Transactions on Neural Networks
Constructing Web User Profiles: A non-invasive Learning Approach
WEBKDD '99 Revised Papers from the International Workshop on Web Usage Analysis and User Profiling
Towards a Subject-Centered Analysis for Automated Video Surveillance
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Cluster-based distributed face tracking in camera networks
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Estimating human body and head orientation change to detect visual attention direction
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
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Identifying human gaze or eye-movement ultimately serves the purpose of identifying an individual's focus of attention. The knowledge of a person's object of interest helps us effectively communicate with other humans by allowing us to identify our conversants' interests, state of mind, and/or intentions. In this paper we propose to track focus of attention of several participants in a meeting. Attention does not necessarily coincide with gaze, as it is a perceptual variable, as opposed to a physical one (eye or head positioning). Automatic tracking focus of attention is therefore achieved by modeling both, the persons head movements as well as the relative locations of probable targets of interest in a room. Over video sequences taken in a meeting situation, the focus of attention could be identified up to 98% of the time.