Bootstrap Initialization of Nonparametric Texture Models for Tracking
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Face Direction Estimation Using Multiple Cameras for Human Computer Interaction
ICMI '00 Proceedings of the Third International Conference on Advances in Multimodal Interfaces
Design issues for vision-based computer interaction systems
Proceedings of the 2001 workshop on Perceptive user interfaces
2006 Special issue: A probabilistic model of gaze imitation and shared attention
Neural Networks - 2006 Special issue: The brain mechanisms of imitation learning
Tracking the multi person wandering visual focus of attention
Proceedings of the 8th international conference on Multimodal interfaces
Recognizing visual focus of attention from head pose in natural meetings
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Head pose tracking and focus of attention recognition algorithms in meeting rooms
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Estimating face pose by facial asymmetry and geometry
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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
Fast detection of frequent change in focus of human attention
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
Exploiting perception for face analysis: image abstraction for head pose estimation
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
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We present an algorithm for estimation of head orientation, given cropped images of a subject's head from any viewpoint. Our algorithm handles dramatic changes in illumination, applies to many people without per-user initialization, and covers a wider range (e.g., side and back) of head orientations than previous algorithms.The algorithm builds an ellipsoidal model of the head, where points on the model maintain probabilistic information about surface edge density. To collect data for each point on the model, edge-density features are extracted from hand-annotated training images and projected onto the model. Each model point learns a probability density function from the training observations. During pose estimation, features are extracted from input images; then, the maximum a posteriori pose is sought, given the current observation.