EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal Component Analysis over Continuous Subspaces and Intersection of Half-Spaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Robust Face Detection Using the Hausdorff Distance
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Multidimensional Morphable Models
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Active Appearance Models Revisited
International Journal of Computer Vision
Fast Active Appearance Model Search Using Canonical Correlation Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning AAM fitting through simulation
Pattern Recognition
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Metric Learning for Image Alignment
International Journal of Computer Vision
Deformable Model Fitting by Regularized Landmark Mean-Shift
International Journal of Computer Vision
Learning deformable shape manifolds
Pattern Recognition
Localizing parts of faces using a consensus of exemplars
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A Least-Squares Framework for Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Globally Optimal Estimation of Nonrigid Image Distortion
International Journal of Computer Vision
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)
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Parameterized Appearance Models (PAMs) such as Active Appearance Models (AAMs), Morphable Models and Boosted Appearance Models have been extensively used for face alignment. Broadly speaking, PAMs methods can be classified into generative and discriminative. Discriminative methods learn a mapping between appearance features and motion parameters (rigid and non-rigid). While discriminative approaches have some advantages (e.g., feature weighting, improved generalization), they suffer from two major drawbacks: (1) they need large amounts of perturbed samples to train a regressor or classifier, making the training process computationally expensive in space and time. (2) It is not practical to uniformly sample the space of motion parameters. In practice, there are regions of the motion space that are more densely sampled than others, resulting in biased models and lack of generalization. To solve these problems, this paper proposes a computationally efficient continuous regressor that does not require the sampling stage. Experiments on real data show the improvement in memory and time requirements to train a discriminative appearance model, as well as improved generalization.