International Journal of Computer Vision
Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
Detection and Recognition of Periodic, Nonrigid Motion
International Journal of Computer Vision
View-Invariant Analysis of Cyclic Motion
International Journal of Computer Vision
Robust Real-Time Periodic Motion Detection, Analysis, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Wide-Range, Person- and Illumination-Insensitive Head Orientation Estimation
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Motion Regularization for Model-Based Head Tracking
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Tracking Focus of Attention in Meetings
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Head Tracking by Active Particle Filtering
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Finding Periodicity in Space and Time
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Recognition of human head orientation based on artificial neural networks
IEEE Transactions on Neural Networks
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We present an algorithm to detect the attentive behavior of persons with frequent change in focus of attention (FCFA) from a static video camera. This behavior can be easily perceived by people as temporal changes of human head pose. Here, we propose to use features extracted by analyzing a similarity matrix of head pose by using a self-similarity measure of the head image sequence. Further, we present a fast algorithm which uses an image vector sequence represented in the principal components subspace instead of the original image sequence to measure the self-similarity. An important feature of the behavior of FCFA is its cyclic pattern where the head pose repeats its position from time to time. A frequency analysis scheme is proposed to find the dynamic characteristics of persons with frequent change of attention or focused attention. A nonparametric classifier is used to classify these two kinds of behaviors (FCFA and focused attention). The fast algorithm discussed in this paper yields real-time performance as well as good accuracy.