An Integrated Model for Accurate Shape Alignment
- Lin Liang ,
- Fang Wen ,
- Xiaoou Tang ,
- Ying-Qing Xu
Published by Association for Computing Machinery, Inc.
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.
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