A Greedy EM Algorithm for Gaussian Mixture Learning
Neural Processing Letters
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
View-Based Active Appearance Models
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Multi-View Face Alignment Using Direct Appearance Models
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
2D Statistical Models of Facial Expressions for Realistic 3D Avatar Animation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A High Resolution Grammatical Model for Face Representation and Sketching
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
On Modelling Nonlinear Shape-and-Texture Appearance Manifolds
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Simplex Optimisation Initialized by Gaussian Mixture for Active Appearance Models
DICTA '07 Proceedings of the 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications
A comparison of shape constrained facial feature detectors
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
| Hi-index | 0.00 |
This paper presents a fast optimization method for active appearance model based on Nelder & Mead simplex in the case of mouth alignment under different expressions. This optimization defines a new constraint space. It uses a Gaussian mixture to initialize and constraint the search of an optimal solution. The Gaussian mixture is applied on the dominant eigenvectors representing the reduced data given by Principal Component Analysis. The new algorithm constraints avoid calculating errors of solutions that don't represent researched forms and textures. The constraint operator added to simplex verifies in each iteration that the solution belongs to the space of research. The tests performed in the context of generalization (learning and testing datasets are different) on two datasets show that our method presents a better convergence rate and less computation time compared to the AAM classically optimized.