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Lip Segmentation Based on Facial Complexion Template

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Book cover Advances in Multimedia Information Processing – PCM 2014 (PCM 2014)

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Abstract

In Traditional Chinese Medicine (TCM), lip diagnosis is an important diagnostic method to judge whether a person is healthy or not. Lip images can reflect the physical conditions of organs in the body. Lip diagnosis has a long history in China and the lips are analyzed by experienced doctors with their nude eyes. This method is not objective and efficient especially in the condition of handling many images. Developing an automatic way to split lips from an image is an important and necessary step. What’s more, lip segmentation can provide improvement in the areas of speech recognition and speaker authentication. To segment lips and facial complexions, many methods are proposed which are based on color spaces such as RGB, HSV, Lab, etc. Other methods are based on different models such as snake, geometry model, etc. This paper proposes a lip segmentation method based on facial complexion template. A facial complexion template can be constructed when the face is detected. We construct the facial complexion template using Hue channel and Saturation channel of color information. By removing the skin similar to facial complexion template values an initial lip image can be got. Finally, by smoothing the lip contour an optimized lip segmentation result can be obtained.

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References

  1. Ghaleh, V., Behrad, A.: Lip contour extraction using RGB color space and fuzzy c-means clustering. In: 2010 IEEE 9th International Conference on Cybernetic Intelligent Systems (CIS). IEEE (2010)

    Google Scholar 

  2. Liewa, A.W.-C., Leung, S.H., Lau, W.H.: Lip Contour Extraction Using a Deformable Model. In: 7th IEEE International Conference on Image Processing, pp. 255–258. IEEE Press, Vancouver (2000)

    Google Scholar 

  3. Delmas, P., Coulon, P.Y., Fristot, V.: Automatic Snakes for Robust Lip Boundaries Ex-traction. In: 5th IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 3069–3072. IEEE Press, Phoenix (1999)

    Google Scholar 

  4. Yan, X., Li, X., Zheng, L., Li, F.: Robust Lip Segmentation Method Based on Level Set Model. In: Qiu, G., Lam, K.M., Kiya, H., Xue, X.-Y., Kuo, C.-C.J., Lew, M.S. (eds.) PCM 2010, Part I. LNCS, vol. 6297, pp. 731–739. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  5. Otsu, N.: A Threshold Selection Method from Gray-level Histograms. IEEE Trans. on Systems, Man, and Cybernetics 9(1), 62–66 (1979)

    Article  MathSciNet  Google Scholar 

  6. Erber, N.P.: Interaction of audition and vision in the recognition of oral speech stimuli. J. Speech Hearing Res. 12, 423–425 (1969)

    Article  Google Scholar 

  7. Zhang, Y., Levinson, S., Huang, T.: Speaker independent audio-visual speech recognition. In: Proc. IEEE Int. Conf. Multimedia and Expo, New York, vol. 2, pp. 1073–1076 (July 2000)

    Google Scholar 

  8. Rabi, G., Lu, S.: Visual speech recognition by recurrent neural networks. In: Engineering Innovation: Voyage of Discovery Electrical and Computer Engineering 1997, St. Johns, Nfld., Canada, vol. 1, pp. 55–58 (May 1997)

    Google Scholar 

  9. Luettin, J., Thacker, N.A., Beet, S.W.: Visual speech recognition using active shape models and hidden Markov models. In: Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, Atlanta, GA, vol. 2, pp. 817–820 (May 1996)

    Google Scholar 

  10. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1. IEEE (2001)

    Google Scholar 

  11. Hsu, R.L., Abdel-Mottaleb, M., Jain, A.K.: Face detection in color images. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 696–706 (2002)

    Article  Google Scholar 

  12. Hammal, Z., Couvreur, L., Caplier, A., Rombaut, M.: Facial expression recognition based on the belief theory: Comparison with different classifiers. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 743–752. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

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© 2014 Springer International Publishing Switzerland

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Sun, C., Lu, H., Zhang, W., Qiu, X., Li, F., Zhang, H. (2014). Lip Segmentation Based on Facial Complexion Template. In: Ooi, W.T., Snoek, C.G.M., Tan, H.K., Ho, CK., Huet, B., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2014. PCM 2014. Lecture Notes in Computer Science, vol 8879. Springer, Cham. https://doi.org/10.1007/978-3-319-13168-9_20

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  • DOI: https://doi.org/10.1007/978-3-319-13168-9_20

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13167-2

  • Online ISBN: 978-3-319-13168-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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