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An Integrated Model for Accurate Shape Alignment

  • Lin Liang
  • Fang Wen
  • Xiaoou Tang
  • Ying-qing Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3954)

Abstract

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.

Keywords

Line Segment Feature Point Optimal Shape Shape Model Global Shape 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lin Liang
    • 1
  • Fang Wen
    • 1
  • Xiaoou Tang
    • 1
  • Ying-qing Xu
    • 1
  1. 1.Visual Computing GroupMicrosoft Research AsiaBeijingChina

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