Adjusting Shape Parameters Using Model-Based Optical Flow Residuals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stable Real-Time 3D Tracking Using Online and Offline Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Model-Based 3D Tracking of Deformable Objects
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Automatic 3D Face Modeling from Video
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Robust Real-Time Face Pose and Facial Expression Recovery
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Face transfer with multilinear models
ACM SIGGRAPH 2006 Courses
Generic vs. person specific active appearance models
Image and Vision Computing
Real-time combined 2D+3D active appearance models
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Real-time non-rigid shape recovery via active appearance models for augmented reality
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Real-time modeling of face deformation for 3d head pose estimation
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
On Appearance Based Face and Facial Action Tracking
IEEE Transactions on Circuits and Systems for Video Technology
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In tracking face and facial actions of unknown people, it is essential to take into account two components of facial shape variations: shape variation between people and variation caused by different facial actions such as facial expressions. This paper presents a monocular method of tracking faces and facial actions using a multilinear face model that treats interpersonal and intrapersonal shape variations separately. We created this method using a multilinear face model by integrating two different frameworks: particle filter-based tracking for time-dependent facial action and pose estimation and incremental bundle adjustment for person-dependent shape estimation. This unique combination together with multilinear face models is the key to tracking faces and facial actions of arbitrary people in real time with no pre-learned individual face models. Experiments using real video sequences demonstrate the effectiveness of our method.