Active shape models and the shape approximation problem
BMVC '95 Proceedings of the 1995 British conference on Machine vision (Vol. 1)
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
3D Alignment of Face in a Single Image
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Multi-View Active Shape Model with Robust Parameter Estimation
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
High-Performance Rotation Invariant Multiview Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face alignment using statistical models and wavelet features
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A novel convergence acheme for active appearance models
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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For multi-view face alignment (MVFA), the non-linear variation of shape and texture, and the self-occlusion of facial feature points caused by view change are the two major difficulties. The state-of-the-art MVFA methods are essentially view-based approaches in which views are divided into several categories such as frontal, half profile, full profile etc. and each of them has its own model in MVFA. Therefore the view estimation problem becomes a critical step in MVFA. In this paper, a MVFA method using 3D face shape model for view estimation is presented in which the 3D shape model is used to estimate the pose of the face thereby selecting its model and indicating its self-occluded points. Experiments on different datasets are reported to show the improvement over previous works.