Active shape models—their training and application
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework for Robust Subspace Learning
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Regularized 3D Morphable Models
HLK '03 Proceedings of the First IEEE International Workshop on Higher-Level Knowledge in 3D Modeling and Motion Analysis
Active Appearance Models Revisited
International Journal of Computer Vision
Automatic feature localisation with constrained local models
Pattern Recognition
Parametric Image Alignment Using Enhanced Correlation Coefficient Maximization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning AAM fitting through simulation
Pattern Recognition
Efficient constrained local model fitting for non-rigid face alignment
Image and Vision Computing
Generic vs. person specific active appearance models
Image and Vision Computing
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
Deformable Model Fitting by Regularized Landmark Mean-Shift
International Journal of Computer Vision
Localizing parts of faces using a consensus of exemplars
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Face detection, pose estimation, and landmark localization in the wild
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Face alignment by Explicit Shape Regression
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Fourier Active Appearance Models
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Robust and efficient parametric face alignment
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Subspace Learning from Image Gradient Orientations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image and Vision Computing
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The proposed Active Orientation Models (AOMs) are generative models of facial shape and appearance. Their main differences with the well-known paradigm of Active Appearance Models (AAMs) are (i) they use a different statistical model of appearance, (ii) they are accompanied by a robust algorithm for model fitting and parameter estimation and (iii) and, most importantly, they generalize well to unseen faces and variations. Their main similarity is computational complexity. The project-out version of AOMs is as computationally efficient as the standard project-out inverse compositional algorithm which is admittedly the fastest algorithm for fitting AAMs. We show that not only does the AOM generalize well to unseen identities, but also it outperforms state-of-the-art algorithms for the same task by a large margin. Finally, we prove our claims by providing Matlab code for reproducing our experiments (http://ibug.doc.ic.ac.uk/resources ).