Active shape models—their training and application
Computer Vision and Image Understanding
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
A Non-Linear Gray-Level Appearance Model Improves Active Shape Model Segmentation
MMBIA '01 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA'01)
Active Appearance Models Revisited
International Journal of Computer Vision
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
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This paper presents a face decorating system, which can do makeup on a face image, such as wearing glass, beard or lipstick. In the framework, an improved face alignment method is proposed to localize the key landmarks of a face, which would be used to locate decorations. Active Shape Models(ASMs), as a robust image alignment method is employed to localize such landmarks. In this work, the authors review the conventional ASMs algorithm for face alignment, and present several improvements on it. It's believed that traditional ASMs is heavily dependent on initial states and prone to local minima. To improve the stability as well as its efficiency, much work is done. First, the eyes are roughly localized in the face area, which are used to initialize the shape model and evaluate the result. Then conventional point distribution model(PDM) is replaced by a newly proposed combined PDM. Experiments on a database containing 200 labelled face images show that the proposed method performs significantly better than traditional ASMs. Finally, the improved method was used to implement a face decorating system.