Fitting Parameterized Three-Dimensional Models to Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pose Estimation by Fusing Noisy Data of Different Dimensions
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast and Globally Convergent Pose Estimation from Video Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optical Flow Constraints on Deformable Models with Applications to Face Tracking
International Journal of Computer Vision
Modeling and Animating Realistic Faces from Images
International Journal of Computer Vision
Linear Pose Estimation from Points or Lines
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Approach to Automatic Recognition of Spontaneous Facial Actions
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
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
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-Based 3D Face Capture with Shape-from-Silhouettes
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Robust and Rapid Generation of Animated Faces from Video Images: A Model-Based Modeling Approach
International Journal of Computer Vision - Special Issue on Research at Microsoft Corporation
Parameterized Models for Facial Animation
IEEE Computer Graphics and Applications
Face detection and tracking in a video by propagating detection probabilities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Head Pose Estimation from Passive Stereo Images
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Dynamic random regression forests for real-time head pose estimation
Machine Vision and Applications
Hi-index | 0.00 |
In this paper, we propose an efficient method that estimates the motion parameters of a human head from a video sequence by using a three-layer linear iterative process. In the innermost layer, we estimate the motion of each input face image in a video sequence based on a generic face model and a small set of feature points. A fast iterative least-square method is used to recover these motion parameters. After that, we iteratively estimate three model scaling factors using multiple frames with the recovered poses in the middle layer. Finally, we update 3D coordinates of the feature points on the generic face model in the outermost layer. Since all iterative processes can be solved linearly, the computational cost is low. Tests on synthetic data under noisy conditions and two real video sequences have been performed. Experimental results show that the proposed method is robust and has good performance.