Active shape models—their training and application
Computer Vision and Image Understanding
The nature of statistical learning theory
The nature of statistical learning theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deformable Contours: Modeling and Extraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Face Detection
International Journal of Computer Vision
Active Appearance Models Revisited
International Journal of Computer Vision
A new efficient SVM-based edge detection method
Pattern Recognition Letters
Extended fitting methods of active shape model for the location of facial feature points
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Lip image segmentation using fuzzy clustering incorporating an elliptic shape function
IEEE Transactions on Image Processing
A geometric approach to Support Vector Machine (SVM) classification
IEEE Transactions on Neural Networks
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Active shape model (ASM) plays an important role in face research such as face recognition, pose estimation and gaze estimation. A crucial step of the common ASM is finding a new position for each facial landmark at each iteration. Mahalanobis distance minimisation is used for this finding, provided there are enough training data such that the grey-level profiles for each landmark following a multivariate Gaussian distribution. However, this condition could not be satisfied in most cases. In this paper, a novel method support vector machine-based active shape model (SVMBASM) is proposed for this task. It approaches the finding task as a small sample size classification problem. Moreover, considering the poor classification performance caused by the imbalanced dataset which contains more negative instances (incorrect candidates for new position) than positive instances (correct candidates for new position), a multi-class classification framework is further proposed. Performance evaluation on Shanghai Jiao Tong University face database shows that the proposed SVMBASM outperforms the original ASM in terms of the average error and average frequency of convergence.