EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal Component Analysis with Missing Data and Its Application to Polyhedral Object Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework for Robust Subspace Learning
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Active Appearance Models Revisited
International Journal of Computer Vision
Fast and accurate active appearance models
VisHCI '06 Proceedings of the HCSNet workshop on Use of vision in human-computer interaction - Volume 56
Multi-View AAM Fitting and Construction
International Journal of Computer Vision
Multi-View AAM Fitting and Construction
International Journal of Computer Vision
Facial expression recognition and synthesis toward construction of quasi-personality
AIKED'07 Proceedings of the 6th Conference on 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases - Volume 6
Adaptive weighting of local classifiers by particle filters for robust tracking
Pattern Recognition
Retexturing in the Presence of Complex Illumination and Occlusions
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Efficient illumination independent appearance-based face tracking
Image and Vision Computing
Gappy PCA Classification for Occlusion Tolerant 3D Face Detection
Journal of Mathematical Imaging and Vision
Structure and appearance features for robust 3D facial actions tracking
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Multi-view face segmentation using fusion of statistical shape and appearance models
Computer Vision and Image Understanding
Occluded facial expression tracking
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Model-based stereo with occlusions
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Active appearance models fitting with occlusion
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
A graphical model based solution to the facial feature point tracking problem
Image and Vision Computing
A robust template tracking algorithm with weighted active drift correction
Pattern Recognition Letters
Joint face alignment with non-parametric shape models
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Generative face alignment through 2.5D active appearance models
Computer Vision and Image Understanding
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Active Appearance Models (AAMs) are generative parametric models that have been successfully used in the past to track faces in video. A variety of video applications are possible, including dynamic head pose and gaze estimation for real-time user interfaces, lip-reading, and expression recognition. To construct an AAM, a number of training images of faces with a mesh of canonical feature points (usually hand-marked) are needed. All feature points have to be visible in all training images. However, in many scenarios parts of the face may be occluded. Perhaps the most common cause of occlusion is 3D pose variation, which can cause self-occlusion of the face. Furthermore, tracking using standard AAM fitting algorithms often fails in the presence of even small occlusions. In this paper we propose algorithms to construct AAMs from occluded training images and to track faces efficiently in videos containing occlusion. We evaluate our algorithms both quantitatively and qualitatively and show successful real-time face tracking on a number of image sequences containing varying degrees and types of occlusions.