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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)

Abstract

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.

Keywords

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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References

  1. 1.
    T. Cootes and C. Taylor. Statistical Models of Appearance for Computer Vision. Technical report, University of Manchester, 2000.Google Scholar
  2. 2.
    T. Cootes and C. Taylor. Constrained Active Appearance Models. In Proceedings of the 8th ICCV, July, 2001.Google Scholar
  3. 3.
    P. Green. Reversible Jump Markov Chain Monte Carlo Computation and Bayesian Model Determination. Biometrika, vol. 82, pp. 711–732, 1995.zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    T. Leung, M. Burl, and P. Perona. Finding Faces in Cluttered Scenes using Random Labeled Graph Matching. In Proceedings of the 5th ICCV, June, 1995.Google Scholar
  5. 5.
    A. Martinez and R. Benavente. The AR Face Database. CVC Technical report, No. 24, June 1998.Google Scholar
  6. 6.
    E. Osuna, R. Freund, and F. Girosi. Training Support Vector Machine: An Application To Face Detection. In Proceedings of CVPR’97, pages 130–136, 1997.Google Scholar
  7. 7.
    P. Philips. H. Moon, P. Pauss, and S. Rivzvi. The FERET Evaluation Methodology for Face Recognition Algorithms. In Proceedings of CVPR’97, pp. 137–143, 1997.Google Scholar
  8. 8.
    S. Roberts, C. Holmes, and D. Denison. Minimum-Entropy Data Partitioning Using Reversible Jump Markoc Chain Monte Carlo. IEEE Transactions on PAMI, 23(8):909–914, August, 2001.Google Scholar
  9. 9.
    H. Rowley, S. Baluja, and T. Kanade. Neural Network-Based Face Detection. IEEE Transactions on PAMI, 20(1), January 1998.Google Scholar
  10. 10.
    H. Schneiderman and T. Kanade. A Statistical Method for 3D Object Detection Applied to Faces and Cars. In Proceedings of the 7th ICCV, May, 2000.Google Scholar
  11. 11.
    K. Sung and T. Poggio. Example-based Learning for View-based Human Face Detection. IEEE Transactions on PAMI, 20(1):39–51, 1998.Google Scholar
  12. 12.
    Z. Tu and S. Zhu. Image Segmentation by Data Driven Markov Chain Monte Carlo. In Proceedings of the 8th ICCV, July, 2001.Google Scholar
  13. 13.
    M. Turk and A. Pentland. Eigenface for Recognition. Journal of Cognitive Neurosciences, pages 71–86, 1991.Google Scholar
  14. 14.
    P. Viola and M. Jones. Robust Real-time Face Detection. In Proceedings of the 8th ICCV, July, 2001.Google Scholar
  15. 15.
    A. Yuille, P. Hallinan, and D. Cohen. Feature Extraction from Faces using Deformable Templates. International Journal of Computer Vision, vol. 8, no. 2, pp. 99–111, 1992.CrossRefGoogle Scholar

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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