Active shape models—their training and application
Computer Vision and Image Understanding
Shape Parameter Optimization for AdaBoosted Active Shape Model
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Accurate Face Alignment using Shape Constrained Markov Network
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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
Three-dimensional shape knowledge for joint image segmentation and pose estimation
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Active shape models with invariant optimal features (IOF-ASMs)
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Cascade MR-ASM for locating facial feature points
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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 take a predefined geometry shape as a constraint for accurate shape alignment. A shape model is divided in two parts: fixed shape and active shape. The fixed shape is a user-predefined simple shape with only a few landmarks which can be easily and accurately located by machine or human. The active one is composed of many landmarks with complex shape contour. When searching an active shape, pose parameter is calculated by the fixed shape. Bayesian inference is introduced to make the whole shape more robust to local noise generated by the active shape, which leads to a compensation factor and a smooth factor for a coarse-to-fine shape search. This method provides a simple and stable means for online and offline shape analysis. Experiments on cheek and face contour demonstrate the effectiveness of our proposed approach.