IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
A Robust Model-Based Approach for 3D Head Tracking in Video Sequences
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Robust Real-Time Face Detection
International Journal of Computer Vision
Stable Real-Time 3D Tracking Using Online and Offline Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Learning for Customizable Head Pose Recognition in Robotic Wheelchair Control
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Accurate Head Pose Tracking in Low Resolution Video
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Simultaneous Facial Action Tracking and Expression Recognition in the Presence of Head Motion
International Journal of Computer Vision
Robust head tracking using 3D ellipsoidal head model in particle filter
Pattern Recognition
Pose Robust Face Tracking by Combining Active Appearance Models and Cylinder Head Models
International Journal of Computer Vision
Efficient particle filtering using RANSAC with application to 3D face tracking
Image and Vision 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
Head pose estimation using stereo vision for human-robot interaction
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Image and Vision Computing
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Tracking the pose of human face has long been an important research topic which has many important applications, and it is particularly challenging with a monocular camera because the depth information is lost due to the perspective projection. This work adopts particle filter with partitioned sampling to decompose the state space of face pose tracking into two subspaces for increasing the sampling efficiency, thus achieving satisfactory performance with fewer particles. The parameters in the first subspace describe the target on image plane, and the parameter in the second subspace is used for the estimate of the face pose in yaw angle direction. For the evaluation of each hypothesis in the second subspace, a statistical learning algorithm called relevance vector machine (RVM) is used to map a face containing image to the pose of the face. The training of RVM is tailored to each detected frontal face, and it takes less than half second, which is suitable for a real-time application. The learning based regression model also presents the insensitive ability to expression variation and unmodeled degree of freedom. The experimental results verify that the combination of particle filter and RVM can efficiently reduce the processing time and add robustness to the performance of the system, thus making this algorithm applicable to human-machine interface with low-cost webcams.