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Extraction and Parameterization of Eye Contour from Monkey Face in Monocular Image

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Advancing Computing, Communication, Control and Management

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 56))

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Abstract

This paper proposes one approach for eye contour extraction and parameterization from monkey face in the monocular image. The possible face regions are first segmented using facial skin color model based on YCbCr color space. The color model is constructed from many collected sample pixels of face regions, and Gaussian Mixture Model is used to represent the distribution of color points. Then the segmented regions are further processed with the help of mathematical morphology operations to locate the face region. The face is size normalized according to face width, and rotation normalized according to two located eyes. Taking the boundaries of eye as initial point set, ACM is adopted to search for more smooth and continuous eye contours. Points on the extracted eye contours are used to fit the quadratic curve with least-squares fitting method. Based on the parameters of quadratic function, angry and calm expressions of monkey face are recognized. Our method can be used for facial expression recognition and emotion decision, which is very helpful for medical science research often taking monkey as the experimental object.

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References

  1. Pylyshyn, Z.W.: Computation and Cognition: Towards a Foundation for Cognitive Science. MIT Press, Cambridge (1984)

    Google Scholar 

  2. Elizabeth, S.P., Emma, J.H., Michael, M.: Measuring emotional processes in animals: the utility of a cognitive approach. Neuroscience and Biobehavioral Reviews 29, 469–491 (2005)

    Article  Google Scholar 

  3. Jones, M.J., Rehg, J.M.: Statistical Color Models with Application to Skin Detection. International Journal of Computer Vision 46(1), 81–96 (2002)

    Article  MATH  Google Scholar 

  4. Phung, S.L., Bouzerdoum, A., Chai, D.: Skin segmentation using color pixel classification: analysis and comparison. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(1), 148–154 (2005)

    Article  Google Scholar 

  5. Belongie, S., Carson, C., Greenspan, H., et al.: Color-and texture-based image segmentation using EM and its application to content-based image retrieval. IEEE Int. Conf. Computer Vision 6(1), 465–473 (1998)

    Google Scholar 

  6. Yang, M.H., Kriegman, D.J., Ahuja, N.: Detecting Faces in Images: A Survey. IEEE Trans. on Pattern Analysis and Machine Intelligence, 34–58 (2002)

    Google Scholar 

  7. Park, J., Seo, J., An, D., et al.: Detection of Human faces using skin color and eyes. In: IEEE International Conference on Multimedia and Expo. (ICME), 30 July- 2 August, vol. 1, pp. 133–136 (2000)

    Google Scholar 

  8. Kakumanu, P., Makrogiannis, S., Bourbakis, N.: A survey of skin-color modeling and detection methods. Pattern Recognition 40(3), 1106–1122 (2007)

    Article  MATH  Google Scholar 

  9. Otsu, N.: A threshold selection method from gray-level histogram. IEEE Trans. Syst. Man Cybernet. 9, 62–66 (1979)

    Article  Google Scholar 

  10. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. Journal of Computer Vision 1(4), 321–331 (1987)

    Article  Google Scholar 

  11. Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models: Their training and application. CVGIP: Image Understanding 61, 38–59 (1995)

    Google Scholar 

  12. Davies, R.H., Twining, C.J., Allen, P.D., Cootes, T.F., Taylor, C.J.: Building optimal 2D Statistical Shape Models. Image and Vision Computing 21, 117–128 (2003)

    Article  Google Scholar 

  13. Roberts, M.G., Cootes, T.F., Adams, J.E.: Robust Active Appearance Models with Iteratively Rescaled Kernels. In: Proc. British Machine Vision Conference, vol. 1, pp. 302–311 (2007)

    Google Scholar 

  14. Matthews, I., Baker, S.: Active Appearance Models. International Journal of Computer Vision 60(2), 135–164 (2004)

    Article  Google Scholar 

  15. Zhang, D.Y., Qu, C.Z., Zhao, J.H., Zhang, Z., Ke, Y.W., Cai, B., Qiao, M.Q., Zhang, H.Y.: Eye Contour Extraction Method from Monocular Image with Monkey Face. In: Proceedings of International Symposium on Intelligent Information Technology Application, Shanghai, China, December 21-22, pp. 636–639 (2008)

    Google Scholar 

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Zhang, D. et al. (2010). Extraction and Parameterization of Eye Contour from Monkey Face in Monocular Image. In: Luo, Q. (eds) Advancing Computing, Communication, Control and Management. Lecture Notes in Electrical Engineering, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05173-9_24

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  • DOI: https://doi.org/10.1007/978-3-642-05173-9_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05172-2

  • Online ISBN: 978-3-642-05173-9

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