Hierarchical Shape Modeling for Automatic Face Localization

  • Ce Liu
  • Heung-Yeung Shum
  • Changshui Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2351)


Many approaches have been proposed to locate faces in an image. There are, however, two problems in previous facial shape models using feature points. First, the dimension of the solution space is too big since a large number of key points are needed to model a face. Second, the local features associated with the key points are assumed to be independent. Therefore, previous approaches require good initialization (which is often done manually), and may generate inaccurate localization. To automatically locate faces, we propose a novel hierarchical shape model (HSM) or multi-resolution shape models corresponding to a Gaussian pyramid of the face image. The coarsest shape model can be quickly located in the lowest resolution image. The located coarse model is then used to guide the search for a finer face model in the higher resolution image. Moreover, we devise a Global and Local (GL) distribution to learn the likelihood of the joint distribution of facial features. A novel hierarchical data-driven Markov chain Monte Carlo (HDDMCMC) approach is proposed to achieve the global optimum of face localization. Experimental results demonstrate that our algorithm produces accurate localization results quickly, bypassing the need for good initialization.


Markov Chain Monte Carlo Feature Point Face Image Gaussian Mixture Model 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 2002

Authors and Affiliations

  • Ce Liu
    • 1
  • Heung-Yeung Shum
    • 1
  • Changshui Zhang
    • 2
  1. 1.Visual Computing GroupMicrosoft Research AsiaBeijingChina
  2. 2.Department of AutomationTsinghua UniversityBeijingChina

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