A Robust Active Appearance Models Search Algorithm
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Learning AAM fitting through simulation
Pattern Recognition
Efficient constrained local model fitting for non-rigid face alignment
Image and Vision Computing
Probabilistic Facial Feature Extraction Using Joint Distribution of Location and Texture Information
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Non-rigid face tracking with enforced convexity and local appearance consistency constraint
Image and Vision Computing
Occluded facial expression tracking
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Generative face alignment through 2.5D active appearance models
Computer Vision and Image Understanding
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Active appearance models (AAMs) are generative parametric models commonly used to track faces in video sequences. A limitation of AAMs is they are not robust to occlusion. A recent extension reformulated the search as an iteratively re-weighted least-squares problem. In this paper we focus on the choice of error function for use in a robust AAM search. We evaluate eight error functions using two performance metrics: accuracy of occlusion detection and fitting robustness. We show for any reasonable error function the performance in terms of occlusion detection is the same. However, this does not mean that fitting performance will be the same. We describe experiments for measuring fitting robustness for images containing real occlusion. The best approach assumes the residuals at each pixel are Gaussianaly distributed, then estimates the parameters of the distribution from images that do not contain occlusion. In each iteration of the search, the error image is used to sample these distributions to obtain the pixel weights.