Active shape models—their training and application
Computer Vision and Image Understanding
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Tracking and Learning Graphs on Image Sequences of Faces
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Multi-View Face Alignment Using Direct Appearance Models
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Face alignment using statistical models and wavelet features
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Gabor texture in active appearance models
Neurocomputing
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This paper presents a novel face alignment method for image sequences that integrates Gabor wavelets with the statistical AAM (Active Appearance Model). First, feature points are characterized using Gabor wavelets and can be individually tracked. A disadvantage of this kind of purely feature-based tracking is that errors accumulate and the feature points loose lock on their corresponding features. To overcome this problem, a face affine transform is used to obtain the initial shape of the AAM model. Finally, the AAM is used to impose global constraints upon the local feature points and to produce an exact alignment. Experimental results demonstrate the ability of the proposed algorithm to accurately align and locate facial features.