Active shape models—their training and application
Computer Vision and Image Understanding
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Active Shape Model Search
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
FloatBoost Learning and Statistical Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of face alignment solutions using statistical learning
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Subspace analysis and optimization for AAM based face alignment
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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The Active Shape Models (ASM) is composed of two parts: the ASM shape model and the ASM search The standard ASM, with the shape variance components all discarded and searching in image subspace and shape subspace independently, has blind searching and unstable search result In this paper, we propose a novel idea, called Optimal Shape Subspace, for optimizing ASM search It is constructed by both main shape and shape variance information It allows the reconstructed shape to vary more than that reconstructed in the standard ASM shape space, hence is more expressive in representing shapes in real life A cost function is developed, based on a careful study on the search process especially regarding relations between the ASM shape model and the ASM search An Optimal Searching method using the feedback provided by the evaluation cost can significantly improve the performance of ASM alignment This is demonstrated by experimental results.