Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Active shape models—their training and application
Computer Vision and Image Understanding
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interpreting Face Images Using Active Appearance Models
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
View-Based Active Appearance Models
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Appearance Models Revisited
International Journal of Computer Vision
Automatic Construction of Active Appearance Models as an Image Coding Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Learning active appearance models from image sequences
VisHCI '06 Proceedings of the HCSNet workshop on Use of vision in human-computer interaction - Volume 56
2D vs. 3D Deformable Face Models: Representational Power, Construction, and Real-Time Fitting
International Journal of Computer Vision
Nonrigid Structure-from-Motion: Estimating Shape and Motion with Hierarchical Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tied Factor Analysis for Face Recognition across Large Pose Differences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning AAM fitting through simulation
Pattern Recognition
Generic vs. person specific active appearance models
Image and Vision Computing
Face view synthesis across large angles
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Lucas-Kanade based entropy congealing for joint face alignment
Image and Vision Computing
Neighborhood-Preserving estimation algorithm for facial landmark points
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
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A major drawback of statistical models of non-rigid, deformable objects, such as the active appearance model (AAM), is the required pseudo-dense annotation of landmark points for every training image. We propose a regression-based approach for automatic annotation of face images at arbitrary pose and expression, and for deformable model building using only the annotated frontal images. We pose the problem of learning the pattern of manual annotation as a data-driven regression problem and explore several regression strategies to effectively predict the spatial arrangement of the landmark points for unseen face images, with arbitrary expression, at arbitrary poses. We show that the proposed fully sparse non-linear regression approach outperforms other regression strategies by effectively modelling the changes in the shape of the face under varying pose and is capable of capturing the subtleties of different facial expressions at the same time, thus, ensuring the high quality of the generated synthetic images. We show the generalisability of the proposed approach by automatically annotating the face images from four different databases and verifying the results by comparing them with a ground truth obtained from manual annotations.