Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Efficient Shape Matching Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Regression and Classification Approaches to Eye Localization in Face Images
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Generative Shape Regularization Model for Robust Face Alignment
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Rotation Invariant Kernels and Their Application to Shape Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian tangent shape model: Estimating shape and pose parameters via bayesian inference
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
An integrated model for accurate shape alignment
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
The Journal of Machine Learning Research
Continuous regression for non-rigid image alignment
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Salient and non-salient fiducial detection using a probabilistic graphical model
Pattern Recognition
Joint view-identity manifold for infrared target tracking and recognition
Computer Vision and Image Understanding
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We propose an approach to shape detection of highly deformable shapes in images via manifold learning with regression. Our method does not require shape key points be defined at high contrast image regions, nor do we need an initial estimate of the shape. We only require sufficient representative training data and a rough initial estimate of the object position and scale. We demonstrate the method for face shape learning, and provide a comparison to nonlinear Active Appearance Model. Our method is extremely accurate, to nearly pixel precision and is capable of accurately detecting the shape of faces undergoing extreme expression changes. The technique is robust to occlusions such as glasses and gives reasonable results for extremely degraded image resolutions.