Active shape models—their training and application
Computer Vision and Image Understanding
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Robust Real-Time Face Detection
International Journal of Computer Vision
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
MORPH: A Longitudinal Image Database of Normal Adult Age-Progression
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Locating Facial Features with an Extended Active Shape Model
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
How effective are landmarks and their geometry for face recognition?
Computer Vision and Image Understanding
Deformable Model Fitting by Regularized Landmark Mean-Shift
International Journal of Computer Vision
A Statistical Method for 2-D Facial Landmarking
IEEE Transactions on Image Processing
Hi-index | 0.00 |
In this work we evaluate three generative techniques for automatic registration of more than 250 face landmarks (annotations). We compare/contrast these techniques based on developing general and a ethnic and gender specific models to detemine whether the specific, ethnic-gender, models can outperform the general model in accurately locating the dense landmarks. Further, we determine which of the three genrative tehcniques are more robust. The three techniques evaluted are the Active Shape Models (ASM), the Active Appearance Model (AAM), and the Constrained Local Model (CLM). In addition this work provides an understanding of the types of landmarks that each technique performs well on and the landmarks that the techniques perform poorly on. Further, it is shown that the performance of STASM and CLM are comparable and better than AAM and that specific models perform better than the general models.