Active shape models—their training and application
Computer Vision and Image Understanding
An Experimental and Theoretical Comparison of Model SelectionMethods
Machine Learning - Special issue on the eighth annual conference on computational learning theory, (COLT '95)
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Interpreting Face Images Using Active Appearance Models
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Using the Active Appearance Algorithm for Face and Facial Feature Tracking
RATFG-RTS '01 Proceedings of the IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS'01)
A review of active appearance models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Multi-modal interview concept detection for rushes exploitation
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
Illumination invariant face alignment using multi-band active appearance model
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Generic facial encoding for shape alignment with active models
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Optimal shape space and searching in ASM based face alignment
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
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Active Appearance Models (AAM) is very powerful for extracting objects, e.g. faces, from images. It is composed of two parts: the AAM subspace model and the AAM search. While these two parts are closely correlated, existing efforts treated them separately and had not considered how to optimize them overall. In this paper, an approach is proposed to optimize the subspace model while considering the search procedure. We first perform a subspace error analysis, and then to minimize the AAM error we propose an approach which optimizes the subspace model according to the search procedure. For the subspace error analysis, we decomposed the subspace error into two parts, which are introduced by the subspace model and the search procedure respectively. This decomposition shows that the optimal results of AAM can be achieved only by optimizing both of them jointly rather than separately. Furthermore, based on this error decomposition, we develop a method to end the optimal subspace model according to the search procedure by considering both the two decomposed errors. Experimental results demonstrate that our method can end the optimal AAM subspace model rapidly and improve the performance of AAM significantly.