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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)

Abstract

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.

Keywords

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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References

  1. 1.
    Mlrhosseinl, A.R., Yan, H., Lam, K.M.: Adaptive Deformable Model for Mouth Boundary Detection. Optical Engineering 37(3), 869–875 (1998)CrossRefGoogle Scholar
  2. 2.
    Oliver, N., Pentland, A.: LAFTER: Lips and Face Real Time Tracker. In: Proceedings of the 1997 Conf. on Computer Vision and Pattern Recognition, pp. 123–129 (1997)Google Scholar
  3. 3.
    Kaucic, R., Blake, A.: Accurate, Real-Time, Unadorned Lip Tracking. In: Proceedings of the 6th International Conf. on Computer Vision, pp. 370–375 (1998)Google Scholar
  4. 4.
    Matthnews, I., Cootes, T.F., Andrew Banghan, J., Cox, S., Marvey, R.: Extractoin of Visual Features for Lipreading. IEEE Tans. on Pattern Recognition and Machine Analysis 24(2), 198–213 (2002)CrossRefGoogle Scholar
  5. 5.
    Zhang, L.: Estimation of the mouth features using deformable templates. In: IEEE International Conference on Image Processing, vol. III, pp. 328–331 (1997)Google Scholar
  6. 6.
    Lievin, M., Luthon, F.: A Hierarchical Segmentation Algorithm for Face Analysis: Application to Lipreading. In: IEEE Conf. on Multimedia & Exposition 2000 (August 2000)Google Scholar
  7. 7.
    Wark, T., Sridharan, S., Chandran, V.: An Approach to Statistical Lip Modelling for Speaker Identification via Chromatic Feature Extraction. In: Proceedings of the 14th International Conf. on Pattern Recognition, vol. 1, pp. 123–125 (1998)Google Scholar
  8. 8.
    Wark, T., Sridharan, S.: A Syntatic Approach to Automatic Lip Feature Extraction for Speaker Identification. In: IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 6, pp. 3693–3696 (1998)Google Scholar
  9. 9.
    Delmas, P., Coulon, Y., Fristot, V.: Automatic Snakes for Robust Lip Boundaries Extraction. In: IEEE International Conf. on Acoustics, Speech and Signal Processing, vol. 6, pp. 3069–3072 (1999)Google Scholar
  10. 10.
    Lievin, M., Luthon, F.: Unsupervised Lip Segmentation under Natural Conditions. In: IEEE International Conf. on Acoustics, Speech and Signal Processing, vol. 6, pp. 3065–3068 (1999)Google Scholar
  11. 11.
    Luettin, J., Thacker, N.A.: Speechreading using probabilistic models. Computer Vision and Image Understanding 65, 163–178 (1997)CrossRefGoogle Scholar
  12. 12.
    Stegmann, M.B., Fisker, R.: ”On Properties of Active Shape Models”, Informatics and Mathematical Modelling, Technical University of Denmark (2000)Google Scholar
  13. 13.
    Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active Shape Models-Their Training and Application. Computer Vision and Image Understanding 61(1), 38–59 (1995)CrossRefGoogle Scholar
  14. 14.
    Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying. IEEE Tans. on Pattern Recognition and Machine Analysis 24(8), 1026–1038 (2002)CrossRefGoogle Scholar
  15. 15.
    Movellan, J.R.: Visual Speech Recognition with Stochastic Networks. Advances in Neural Information Processing System, vol. 7. MIT Press, Cambridge (1995)Google Scholar

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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