Active shape models—their training and application
Computer Vision and Image Understanding
Face Recognition Using Active Appearance Models
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Interpreting Face Images Using Active Appearance Models
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Robust Real-Time Face Detection
International Journal of Computer Vision
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Active Appearance Models Revisited
International Journal of Computer Vision
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Computational Statistics Handbook with MATLAB, Second Edition (Chapman & Hall/Crc Computer Science & Data Analysis)
Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bosphorus Database for 3D Face Analysis
Biometrics and Identity Management
Learning AAM fitting through simulation
Pattern Recognition
Noncontact automatic heart rate analysis in visible spectrum by specific face regions
Proceedings of the 14th International Conference on Computer Systems and Technologies
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The active appearance model (AAM) has proven to be a powerful tool for modeling deformable visual objects. AAMs are nonlinear parametric models in terms of the relation between the pixel intensities and the parameters of the model. In this paper, we propose a fitting procedure for a 3D AAM based on kernel methods for regression. The use of kernel functions provides a powerful way of detecting nonlinear relations using linear algorithms in an appropriate feature space. For analysis, we have chosen the relevance vector machines (RVM) and the kernel ridge method. The statistics computed on data generated with our 3D AAM implementation show that the kernel methods give better results compared to the linear regression models. Although they are less computational efficient, due to their higher accuracy the kernel methods have the advantage of reducing the searching space for the 3D AAM fitting algorithm.