Skip to main content
SpringerLink
Log in

Detecting Facial Features by Heteroassociative Memory Neural Network Utilizing Facial Statistics

  • Conference paper
Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3972))

Included in the following conference series:

  • 79 Accesses

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.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. 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. Fausett, L.: Fundamentals of Neural Networks, Architectures, Algorithms and Applications, pp. 101–125. Prentice Hall, Englewood Cliffs (1994)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. Mould, R.F.: Introductory Medical Statistics, Institute of Physics Publishing Bristol and Philadelphia, 3rd edn., pp. 115–128 (1998)

    Google Scholar 

  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. Sharma, G.: Digital Color Imaging Handbook, pp. 574–587. CRC Press, New York (2003)

    Google Scholar 

  10. Shih, F.Y., Chung, C.F.: Automatic Extraction of Head and Face Boundaries and Facial Features. Information Sciences 158, 117–130 (2004)

    Article  Google Scholar 

  11. Yang, J., Waibel, A.: A Real-Time Face Tracker. Proceeding of WACV. In: Proceedings 3rd IEEE Workshop, pp. 142–147 (1996)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Biomedical Engineering, College of Biomedical & Health Science, Konkuk University, Chungju, 380-701, Korea

    Kyeong-Seop Kim, Tae-Ho Yoon & Seung-Won Shin

Authors
  1. Kyeong-Seop Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tae-Ho Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seung-Won Shin
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kim, KS., Yoon, TH., Shin, SW. (2006). Detecting Facial Features by Heteroassociative Memory Neural Network Utilizing Facial Statistics. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_7

Download citation

  • DOI: https://doi.org/10.1007/11760023_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34437-7

  • Online ISBN: 978-3-540-34438-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation