Active shape models—their training and application
Computer Vision and Image Understanding
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Automatic Interpretation and Coding of Face Images Using Flexible Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Appearance Models Revisited
International Journal of Computer Vision
Real-time combined 2D+3D active appearance models
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A model based method for automatic facial expression recognition
ECML'05 Proceedings of the 16th European conference on Machine Learning
A comparative study of facial appearance modeling methods for active appearance models
Pattern Recognition Letters
A review of active appearance models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Pose robust face tracking by combining view-based AAMs and temporal filters
Computer Vision and Image Understanding
Learning realistic facial expressions from web images
Pattern Recognition
A Reflexion on Implementation Version for Active Appearance Model
International Journal of Computer Vision and Image Processing
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Active Appearance Model (AAM) framework is a very useful method that can fit the shape and appearance model to the input image for various image analysis and synthesis problems. However, since the goal of the AAM fitting algorithm is to minimize the residual error between the model appearance and the input image, it often fails to accurately converge to the landmark points of the input image. To alleviate this weakness, we have combined Active Shape Models (ASM) into AAMs, in which ASMs try to find correct landmark points using the local profile model. Since the original objective function of the ASM search is not appropriate for combining these methods, we derive a gradient based iterative method by modifying the objective function of the ASM search. Then, we propose a new fitting method that combines the objective functions of both ASM and AAM into a single objective function in a gradient based optimization framework. Experimental results show that the proposed fitting method reduces the average fitting error when compared with existing fitting methods such as ASM, AAM, and Texture Constrained-ASM (TC-ASM) and improves the performance of facial expression recognition significantly.