Skip to main content

Efficient Facial Features Warping Using BSM (Bayesian Shape Model)

  • Conference paper
Computational Science and Its Applications – ICCSA 2008 (ICCSA 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5073))

Included in the following conference series:

  • 1577 Accesses

Abstract

This paper proposes an efficient method for warping facial features. The existing methods use points, which are standard for facial features warping, without the reason or calculation. And existing methods have difficulties for facial feature warping. We estimate the standard points by using BSM(Bayeian Shape Model). From the experiment results for the various image, the proposed algorithm shows more natural results than the conventional algorithm and is more efficient than ASM(Active shape model).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Knappmeyer, B., Thornton, I.M., Bulthoff, H.H.: The use of facial motion and facial form during the processing of identity. Vision research 43(18), 1921–1936 (2003)

    Article  Google Scholar 

  2. Su, C., Zhuang, H., Huang, L., Wu, F.: Bayesian Network Enhanced Prediction Based Multiple Facial Feature Tracking. Journal of image and graphics 10(2), 175–180 (2005)

    Google Scholar 

  3. Xue, Z., Li, S.Z., Shen, D., Teoh, E.K.: A novel Bayesian shape model for facial feature extraction. IEEE Transactions on Patten Analysis and Machine Intelligence 1, 514–519 (2002)

    Google Scholar 

  4. Wan, K.W., Lam, K.M., Ng, K.C.: An accurate active shape model for facial feature extraction. Pattern recognition letters 26(15), 2409–2423 (2005)

    Article  Google Scholar 

  5. Xue, Z., Li, S.Z., Lu, J., Teoh, E.K.: A Bayesian Model for Extracting Facial Features. Pattern recognition 36(12), 2819–2833 (2003)

    Article  MATH  Google Scholar 

  6. Lai, K.F., Chin, R.T.: Deformable contours: modeling and extraction. IEEE Transactions on Patten Analysis and Machine Intelligence 17(11), 1084–1090 (1995)

    Article  Google Scholar 

  7. 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)

    Article  Google Scholar 

  8. Cootes, T., Edwards, G.J., Taylor, C.J.: models. In: Proceeding of 5th European Conference on Computer Vision, vol. 2, pp. 484–498 (1998)

    Google Scholar 

  9. Cootes, T.F., Taylor, C.J.: Combining elastic and statistical models of appearance variation. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 149–163. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  10. Moghaddam, B., Pentland, A.: Probabilistic visual learninig for object representation. IEEE Transactions on Patten Analysis and Machine Intelligence 19(7), 696–710 (1997)

    Article  Google Scholar 

  11. Xue, Z., Li, S.Z., Teoh, E.K.: Ai-eigensnake: an affine-invariant deformable contour model for object matching. Image Vision Computing 20(2), 77–84 (2002)

    Article  Google Scholar 

  12. Jain, A.K., Zhong, Y., Lakshmanan, S.: Object matching using deformable templates. IEEE Trans. Pattern Anal. Mach. Intell 18(3), 267–278 (1996)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Osvaldo Gervasi Beniamino Murgante Antonio Laganà David Taniar Youngsong Mun Marina L. Gavrilova

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kwon, JH., Kim, GY., Mun, Y. (2008). Efficient Facial Features Warping Using BSM (Bayesian Shape Model). In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2008. ICCSA 2008. Lecture Notes in Computer Science, vol 5073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69848-7_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-69848-7_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69840-1

  • Online ISBN: 978-3-540-69848-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics