Rotation-Invariant Facial Feature Detection Using Gabor Wavelet and Entropy

  • Ehsan Fazl Ersi
  • John S. Zelek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3656)

Abstract

A novel technique for facial feature detection in images of frontal faces is presented. We use a set of Gabor wavelet coefficients in different orientations and frequencies to analyze and describe facial features. However, due to the lack of sufficient local structures for describing facial features, Gabor wavelets can not perfectly capture the wide range of possible variations in the appearance of facial features, and thus can give many false positive (and sometimes false negative) responses. We show that the performance of such a feature detector can be significantly improved by using the local entropy of features. Complex regions in a face image, such as the eye, exhibit unpredictable local intensity and hence high entropy. Our method is robust against image rotation, varying brightness, varying contrast and a certain amount of scaling.

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References

  1. 1.
    Wiskott, L., Fellous, J., Kruger, N., Malsburg, C.: Face Recognition by Elastic Bunch Graph Matching. Intelligent Biometric Techniques in Fingerprint and Face Recognition, 355–396 (1999)Google Scholar
  2. 2.
    Liao, R., Li, S.Z.: Face Recognition Based on Multiple Features. In: Automatic Face and Gesture Recognition, fourth IEEE International Conference, pp. 34–39 (2000)Google Scholar
  3. 3.
    Toyama, K., Feris, R.S., Gemmell, J., Krüger, V.: Hierarchical Wavelet Networks for Facial Feature Localization. In: 5th International Conference on Automatic Face and Gesture Recognition (2002)Google Scholar
  4. 4.
    Cristinacce, D., Cootes, T.: Facial Feature Detection Using AdaBoost with Shape Constraints. In: British Machine Vision Conference, BMVC (2003)Google Scholar
  5. 5.
    Viola, P., Jones, M.: Rapid Object Detection Using a Boosted Cascade of Simple Features. In: Computer Vision and Pattern Recognition Conference, vol. 1, pp. 511–518 (2001)Google Scholar
  6. 6.
    Dryden, I., Mardia, K.V.: The Statistical Analysis of Shape. Wiley, London (1998)Google Scholar
  7. 7.
    D’Orazio, T., Leo, M., Cicirelli, G., Distante, A.: An Algorithm for Real Time Eye Detection in Face Images. In: 17th International Conference on Pattern Recognition, vol. 3, pp. 278–281 (2004)Google Scholar
  8. 8.
    Donato, G., Stewart, M., Hager, J., Ekman, P., Sejnowski, T.: Classifying Facial Actions. IEEE Transaction on Pattern Analysis and Machine Intelligence 21 (1999)Google Scholar
  9. 9.
    Timor Kadir, A.Z., Bardy, M.: An Affine Invariant Salient Region Detector. In: European conference on Computer Vision, pp. 228–241 (2004)Google Scholar
  10. 10.
    Chow, G., Li, X.: Towards a System for Automatic Facial Feature Detection. Pattern Recognition 26, 1739–1755 (1993)CrossRefGoogle Scholar
  11. 11.
    Yang, G., Huang, T.: Human Face Detection in a Complex Background. Pattern Recognition 27, 53–63 (1994)CrossRefGoogle Scholar
  12. 12.
    Chang, T., Huang, T., Novak, C.: Facial Feature Extraction from Color Images. In: International Conference on Pattern Recognition, pp. 39–43 (1994)Google Scholar
  13. 13.
    Huang, C., Cheng, T., Chen, C.: Color Image Segmentation Using Scale Space Filter and Markov Random Field. Pattern Recognition 25, 1217–1229 (1992)CrossRefGoogle Scholar
  14. 14.
  15. 15.
    Florack, L., Romeny, B., Viergever, M., Koenderink, J.: The Gaussian Scale-space Paradigm and the Multi-scale Local Jet. International Journal of Computer Vision 18, 61–75 (1996)CrossRefGoogle Scholar
  16. 16.
    Yokono, J.J., Poggio, T.: Rotation Invariant Object Recognition from One Training Example. Massachusetts Institute of Technology, CBCL Memo 238 (2004)Google Scholar
  17. 17.
    Lowe, D.: Distinctive Image Features from Scale-Invariant Key-points. International Journal of Computer Vision (2004)Google Scholar
  18. 18.
    MacKay, D.: Information Theory, Inference, and Learning Algorithms. Cambridge University Press, Cambridge (2003) ISBN 0-521-64298-1MATHGoogle Scholar
  19. 19.
    Hjelmas, E., Lowe, B.: Face Detection: A Survey. Computer Vision and Image Understanding, 235–274 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ehsan Fazl Ersi
    • 1
  • John S. Zelek
    • 1
  1. 1.Department of System Design EngineeringUniversity of WaterlooWaterlooCanada

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