Advertisement

Detecting Facial Features by Heteroassociative Memory Neural Network Utilizing Facial Statistics

  • Kyeong-Seop Kim
  • Tae-Ho Yoon
  • Seung-Won Shin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

In this paper, we present an efficient algorithm of extracting the multiple facial features such as eyes, nose, and mouth. The face candidates are first obtained based on skin-color filtering inYC b C r color domain and skin-temperature values and then the elliptic measures are applied to extract a true face candidate and its boundary. A Sobel edge mask is performed and consequently horizontal projection operation is applied to locate the eyes referring to the maximum horizontal projection value in Y component. Once two eyes are located, the distance that crosses the center of eyes and extends to the face boundary, D 1 is determined. A heteroassociative memory neural network model is utilized to find the facial features. An input neuron vector X accepts D 1 and the output neurons vector Y maps it to the facial features such as eyes, nose and mouth.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Birchfield, S.: An Elliptical Head Tracker. In: Proceeding of the 31st Asilmor, Conference on Signals, Systems and Computers, pp. 1710–1714 (1997)Google Scholar
  2. 2.
    Fausett, L.: Fundamentals of Neural Networks, Architectures, Algorithms and Applications, pp. 101–125. Prentice Hall, Englewood Cliffs (1994)zbMATHGoogle Scholar
  3. 3.
    Genno, H., Saijo, A., Yoshida, H., Suzuki, R., Osumi, M.: Non-Contact Method for Measuring Facial Skin Temperature. Industrial Ergonomics 19, 147–159 (1997)CrossRefGoogle Scholar
  4. 4.
    Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using MATLAB, pp. 334–358. Prentice Hall, Englewood Cliffs (2004)Google Scholar
  5. 5.
    Kim, K.S., Yoon, T.H., Han, M.H., Shin, S.W., Kim, I.Y., Lee, S.M.: Face Recognition and Thermal Feature Extraction for Health Monitoring. In: International Ubiquitous Healthcare Conference, pp. 85–86 (2004)Google Scholar
  6. 6.
    Kim, Y.G., Han, J.H., Ahn, J.H.: Facial Regions Detection Using the Color and Shape Information in Color Still Images. Journal of Korea Multimedia Society 4, 67–74 (2001)Google Scholar
  7. 7.
    Mould, R.F.: Introductory Medical Statistics, Institute of Physics Publishing Bristol and Philadelphia, 3rd edn., pp. 115–128 (1998)Google Scholar
  8. 8.
    Quian, R.J., Sezan, M.I., Matthews, K.E.: A Robust Real-Time Face Tracking Algorithms. In: International Conference on Image Processing, pp. 131–135 (1998)Google Scholar
  9. 9.
    Sharma, G.: Digital Color Imaging Handbook, pp. 574–587. CRC Press, New York (2003)Google Scholar
  10. 10.
    Shih, F.Y., Chung, C.F.: Automatic Extraction of Head and Face Boundaries and Facial Features. Information Sciences 158, 117–130 (2004)CrossRefGoogle Scholar
  11. 11.
    Yang, J., Waibel, A.: A Real-Time Face Tracker. Proceeding of WACV. In: Proceedings 3rd IEEE Workshop, pp. 142–147 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kyeong-Seop Kim
    • 1
  • Tae-Ho Yoon
    • 1
  • Seung-Won Shin
    • 1
  1. 1.School of Biomedical Engineering, College of Biomedical & Health ScienceKonkuk UniversityChungjuKorea

Personalised recommendations