Learning to track the visual motion of contours
Artificial Intelligence - Special volume on computer vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Introduction to Monte Carlo methods
Learning in graphical models
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
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
Colour Model Selection and Adaption in Dynamic Scenes
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Tracking Facial Feature Points with Gabor Wavelets and Shape Models
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Tracking and Learning Graphs and Pose on Image Sequences of Faces
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Robust Face Tracking Using Color
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Large Motion Object Tracking using Active Contour Combined Active Appearance Model
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
Robust face-tracking using skin color and facial shape
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Background robust face tracking using active contour technique combined active appearance model
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Efficient multiple faces tracking based on Relevance Vector Machine and Boosting learning
Journal of Visual Communication and Image Representation
Dynamic appearance model for particle filter based visual tracking
Pattern Recognition
Engineering Applications of Artificial Intelligence
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This paper proposes a robust face tracking method based on the condensation algorithm that uses skin color and facial shape as observation measures. Two trackers are used for robust tracking: one tracks the skin color regions and the other tracks the facial shape regions. The two trackers are coupled using an importance sampling technique, where the skin color density obtained from the skin color tracker is used as the importance function to generate samples for the shape tracker. The samples of the skin color tracker within the chosen shape region are updated with higher weights. Also, an adaptive color model is used to avoid the effect of illumination change in the skin color tracker. The proposed face tracker performs more robustly than either the skin-color-based tracker or the facial shape-based tracker, given the presence of background clutter and/or illumination changes.