Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Active shape models—their training and application
Computer Vision and Image Understanding
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Hierarchical Shape Modeling for Automatic Face Localization
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Finding Deformable Shapes Using Loopy Belief Propagation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Tracking Objects Using Density Matching and Shape Priors
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Automatic 3D reconstruction for face recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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
Learning deformable shape manifolds
Pattern Recognition
Active shape model based on sparse representation
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
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In this paper, we propose a two-level integrated model for accurate face shape alignment. At the low level, the shape is split into a set of line segments which serve as the nodes in the hidden layer of a Markov Network. At the high level, all the line segments are constrained by a global Gaussian point distribution model. Furthermore, those already accurately aligned points from the low level are detected and constrained using a constrained regularization algorithm. By analyzing the regularization result, a mask image of local minima is generated to guide the distribution of Markov Network states, which makes our algorithm more robust. Extensive experiments demonstrate the accuracy and effectiveness of our proposed approach.