Lip Localization Based on Active Shape Model and Gaussian Mixture Model

  • Kyung Shik Jang
  • Soowhan Han
  • Imgeun Lee
  • Young Woon Woo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4319)


This paper describes an efficient method for locating lip. Lip deformation is modeled by a statistically deformable model based on Active Shape Model(ASM). In ASM based methods, it is assumed that a training set forms a cluster in shape parameter space. However if there are some clusters in shape parameter space due to an incorrect position of landmark point, ASM may not be able to locate new examples accurately. In this paper, Gaussian mixture is used to characterize the distribution of shape parameter. The Expectation Maximization algorithm is used to determine the maximum likelihood parameters of Gaussian mixture. During search, we resolved the updated locations by projecting a shape into the shape parameter space by using Gaussian mixture. The experiment was performed on many images, and showed very encouraging result.


Shape Parameter Gaussian Mixture Model Deformable Model Landmark Point Active Shape Model 
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

  • Kyung Shik Jang
    • 1
  • Soowhan Han
    • 1
  • Imgeun Lee
    • 2
  • Young Woon Woo
    • 1
  1. 1.Department of Multimedia EngineeringDong-Eui UniversityPusanKorea
  2. 2.Department of Film and Visual EngineeringDong-Eui UniversityPusanKorea

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