Robust Facial Feature Location on Gray Intensity Face

  • Qiong Wang
  • Chunxia Zhao
  • Jingyu Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)


In this paper, we propose an efficient algorithm for facial feature location on gray intensity face. Complex regions in a face image, such as the eye, exhibit unpredictable local intensity and hence high entropy. We use this characteristic to obtain eye candidates, and then these candidates are sent to a classifier to get real eyes. According to the geometry relationship of human face, mouth search region is specified by the coordinates of the left eye and the right eye. And then precise mouth detection is done. Experimental results demonstrate the effectiveness of the proposed method.


Facial feature location image entropy SVM classifier maximum-minimum filter 


  1. 1.
    Cristinacce, D., Cootes, T.: Facial Feature Detection Using AdaBoost with Shape Constraints. In: Proceedings of British Machine Vision Conference, pp. 231–240 (2003)Google Scholar
  2. 2.
    Viola, P., Jones, M.: Rapid Object Detection Using a Boosted Cascade of Simple Features. In: Proceedings of Computer Vision and Pattern Recognition Conference, vol. 1, pp. 511–518 (2001)Google Scholar
  3. 3.
    Dryden, I., Mardia, K.V.: The Statistical Analysis of Shape. Wiley, London (1998)zbMATHGoogle Scholar
  4. 4.
    D’Orazio, T., Leo, M., Cicirelli, G., Distante, A.: An Algorithm for Real Time Eye Detection in Face Images. In: Proceedings of 17th International Conference on Pattern Recognition, vol. 3, pp. 278–281 (2004)Google Scholar
  5. 5.
    Du, S., Ward, R.: A Robust Approach for Eye Localization Under Variable Illuminations. In: Proceedings of International Conference on Image Processing, vol. 1, pp. 377–380 (2007)Google Scholar
  6. 6.
    Toews, M., Arbel, T.: Entropy-of-likelihood Feature Selection for Image Correspondence. In: Proceedings of 9th International Conference on Computer Vision, vol. 2, pp. 1041–1047 (2003)Google Scholar
  7. 7.
    Shannon, C.E., Waver, W.: A Mathematical Theory of Communication. Bell System Technical Journal 27, 379–423 (1948)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)CrossRefzbMATHGoogle Scholar
  9. 9.
    Sung, K.K., Poggio, T.: Example-based Learning for View-based Human Face Detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 39–51 (1998)CrossRefGoogle Scholar
  10. 10.
    Oh, J.-S., Kim, D.-W., Kim, J.-T., Yoon, Y.-I., Choi, J.-S.: Facial component detection for efficient facial characteristic point extraction. In: Kamel, M.S., Campilho, A.C. (eds.) ICIAR 2005. LNCS, vol. 3656, pp. 1125–1132. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust face detection using the hausdorff distance. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 90–95. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  12. 12.
    Zhou, Z.H., Geng, X.: Projection Functions for Eye Detection. Pattern Recognition 37, 1049–1056 (2004)CrossRefzbMATHGoogle Scholar
  13. 13.
    Ma, Y., Ding, X.Q., et al.: Robust Precise Eye Location under Probabilistic Framework. In: Proceedings of FGR, pp. 339–344 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Qiong Wang
    • 1
  • Chunxia Zhao
    • 1
  • Jingyu Yang
    • 1
  1. 1.School of Computer Science and TechnologyNanjing University of Science and Technology, Email: nustdaisy@gmail.comNanjingChina

Personalised recommendations