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
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Tracking People with Twists and Exponential Maps
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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
Real Time Tracking and Modeling of Faces: An EKF-Based Analysis by Synthesis Approach
MPEOPLE '99 Proceedings of the IEEE International Workshop on Modelling People
Motion Regularization for Model-Based Head Tracking
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Robust Full-Motion Recovery of Head by Dynamic Templates and Re-Registration Techniques
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Active Appearance Models Revisited
International Journal of Computer Vision
Stable Real-Time 3D Tracking Using Online and Offline Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constructing and Fitting Active Appearance Models With Occlusion
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Real-time combined 2D+3D active appearance models
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
3D facial pose tracking in uncalibrated videos
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
An investigation of model bias in 3d face tracking
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Exploiting facial expressions for affective video summarisation
Proceedings of the ACM International Conference on Image and Video Retrieval
A natural head pose and eye gaze dataset
Proceedings of the International Workshop on Affective-Aware Virtual Agents and Social Robots
Real-time face tracking and pose estimation with partitioned sampling and relevance vector machine
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Multimedia Tools and Applications
Head pose estimation with one camera, in uncalibrated environments
Proceedings of the 2010 workshop on Eye gaze in intelligent human machine interaction
Pose robust face tracking by combining view-based AAMs and temporal filters
Computer Vision and Image Understanding
A review of motion analysis methods for human Nonverbal Communication Computing
Image and Vision Computing
3D Active Appearance Model alignment using intensity and range data
Robotics and Autonomous Systems
Facial expression recognition based on anatomy
Computer Vision and Image Understanding
Audiovisual diarization of people in video content
Multimedia Tools and Applications
Visual Focus of Attention in Non-calibrated Environments using Gaze Estimation
International Journal of Computer Vision
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The active appearance models (AAMs) provide the detailed descriptive parameters that are useful for various autonomous face analysis problems. However, they are not suitable for robust face tracking across large pose variation for the following reasons. First, they are suitable for tracking the local movements of facial features within a limited pose variation. Second, they use gradient-based optimization techniques for model fitting and the fitting performance is thus very sensitive to initial model parameters. Third, when their fitting is failed, it is difficult to obtain appropriate model parameters to re-initialize them. To alleviate these problems, we propose to combine the active appearance models and the cylinder head models (CHMs), where the global head motion parameters obtained from the CHMs are used as the cues of the AAM parameters for a good fitting or re-initialization. The good AAM parameters for robust face tracking are computed in the following manner. First, we estimate the global motion parameters by the CHM fitting algorithm. Second, we project the previously fitted 2D shape points onto the 3D cylinder surface inversely. Third, we transform the inversely projected shape points by the estimated global motion parameters. Fourth, we project the transformed 3D points onto the input image and computed the AAM parameters from them. Finally, we treat the computed AAM parameters as the initial parameters for the fitting. Experimental results showed that face tracking combining AAMs and CHMs is more pose robust than that of AAMs in terms of 170% higher tracking rate and the 115% wider pose coverage.