Merging and Arbitration Strategy Applied Bayesian Classification for Eye Location

  • Eun Jin Koh
  • Phill Kyu Rhee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4109)


Based on template facial features and image segmentation, this paper demonstrates a novel method for automatic detection of eyes in grayscale still images. A decision model of eye location is instituted by the priori knowledge of template facial features. After roughly detection of face, we apply three steps for system to locate eyes. Firstly, the Bayesian eye detector is used to find eye patterns in the upper region of the face image. This vector based Bayesian classifier adopts Haar transform as vectorize because we know that is robust at illumination variation. Secondly, merging and arbitration strategy are applied. It can manage variations of around eye regions due to spectacle rims or eye brows. Finally, Gaussian-projection function can locate robust precision eye position. The experimental results show that the proposed method can achieve higher performance at any test data.


Face Recognition Face Image Mahalanobis Distance Face Detection False Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Jesorsky, O., Kirchberg, K., Frischholz, R.: 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
  2. 2.
    Zhou, H., Geng, X.: Projection functions for eye detection. Pattern Recognition (in press, 2004)Google Scholar
  3. 3.
    Ma, Y., Ding, X., Wang, Z., Wang, N.: Robust precise eye location under probabilistic framework. In: IEEE International Conference on Automatic Face and Gesture Recognition (2004)Google Scholar
  4. 4.
    Rowley, H.A., Baluja, S., Kanade, T.: Neural Network-Based Face Detection. IEEE Trans. Pattern Analysis and Machine Intelligence 20(1) (1998)Google Scholar
  5. 5.
    Liu, C.: A Bayesian Discriminating Features Method for Face Detection. IEEE Trans. Pattern Analysis and Machine Intelligence 25(6), 725–740 (2003)CrossRefGoogle Scholar
  6. 6.
    Liu, C., Wechsler, H.: Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition. IEEE Trans. Image Processing 11(4), 467–476 (2002)CrossRefGoogle Scholar
  7. 7.
    Lucey, S., Sridharan, S., Chandran, V.: Improved facial feature detection for AVSP via unsupervised clustering and dicriminant analysis. EURASIP Journal on Applied Signal Processing 3, 264–275 (2003)Google Scholar
  8. 8.
    Kawaguchi, T., Hikada, D., Rizon, M.: Detection of the eyes from human faces by hough transform and separability filter. In: Proc. of ICIP, pp. 49–52 (2000)Google Scholar
  9. 9.
    Viola, P., Jones, M.: Rapid object detection using a Boosted cascade of simple features. In: Proc. of IEEE Conf. on CVPR, pp. 511–518 (2001)Google Scholar
  10. 10.
    Yongsheng Gao, L.: Face recognition using line edge map. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(6), 764–779 (2002)CrossRefGoogle Scholar
  11. 11.
    Huang, J., Shao, X.H., Wechsler, H.: Pose Discrimination and Eye Detection Using Support Vector Machines. In: Proceeding of NATO-ASI on Face Recognition: From Theory to Applications (1998)Google Scholar
  12. 12.
    Schneiderman, H., Kanade, T.: A statistical model for 3D object detection applied to faces and cars. In: Proc. of IEEE Conf. on CVPR (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Eun Jin Koh
    • 1
  • Phill Kyu Rhee
    • 1
  1. 1.Dept. of Computer Science & EngineeringInha University, Biometrics Engineering Research CenterIncheonKorea

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