Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Recognizing Human Facial Expressions From Long Image Sequences Using Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Recognizing Action Units for Facial Expression Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Towards Improved Observation Models for Visual Tracking: Selective Adaptation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Modeling, Tracking and Interactive Animation of Faces and Heads Using Input from Video
CA '96 Proceedings of the Computer Animation
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
A Probabilistic Framework for Rigid and Non-Rigid Appearance Based Tracking and Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
An Adaptive Fusion Architecture for Target Tracking
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Real-Time, Fully Automatic Upper Facial Feature Tracking
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Head Tracking by Active Particle Filtering
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Tracking Articulated Body by Dynamic Markov Network
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
An active model for facial feature tracking
EURASIP Journal on Applied Signal Processing
Nonparametric belief propagation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
PAMPAS: real-valued graphical models for computer vision
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A graphical model based solution to the facial feature point tracking problem
Image and Vision Computing
SAMT'10 Proceedings of the 5th international conference on Semantic and digital media technologies
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It is challenging to track multiple facial features simultaneously when rich expressions are presented on a face. We propose a two-step solution. In the first step, several independent condensation-style particle filters are utilized to track each facial feature in the temporal domain. Particle filters are very effective for visual tracking problems; however multiple independent trackers ignore the spatial constraints and the natural relationships among facial features. In the second step, we use Bayesian inference--belief propagation--to infer each facial feature's contour in the spatial domain, in which we learn the relationships among contours of facial features beforehand with the help of a large facial expression database. The experimental results show that our algorithm can robustly track multiple facial features simultaneously, while there are large interframe motions with expression changes.