Active shape models and the shape approximation problem
BMVC '95 Proceedings of the 1995 British conference on Machine vision (Vol. 1)
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Hierarchical Shape Modeling for Automatic Face Localization
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Ranking Prior Likelihood Distributions for Bayesian Shape Localization Framework
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Active Appearance Models Revisited
International Journal of Computer Vision
A Bayesian Mixture Model for Multi-View Face Alignment
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Shape Parameter Optimization for AdaBoosted Active Shape Model
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Face alignment using statistical models and wavelet features
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Cast indexing for videos by NCuts and page ranking
Proceedings of the 6th ACM international conference on Image and video retrieval
Robust: real-time 3D face tracking from a monocular view
Journal on Image and Video Processing
Augmented makeover based on 3D morphable model
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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Robust face alignment is crucial for many face processing applications. As face detection only gives a rough estimation of face region, one important problem is how to align facial shapes starting from this rough estimation, especially on face images with expression and pose changes. We propose a novel method of face alignment by building a hierarchical classifier network, connecting face detection and face alignment into a smooth coarse-to-fine procedure. Classifiers are trained to recognize feature textures in different scales from entire face to local patterns. A multi-layer structure is employed to organize the classifiers, which begins with one classifier at the first layer and gradually refines the localization of feature points by more classifiers in the following layers. A Bayesian framework is configured for the inference of the feature points between the layers. The boosted classifiers detects facial features discriminately from its local neighborhood, while the inference between the layers constrains the searching space. Extensive experiments are reported to show its accuracy and robustness.