Merging and Arbitration Strategy for Robust Eye Location

  • Eun Jin Koh
  • Phill Kyu Rhee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4251)


The difficulties of eye location are mainly caused by the variations of intrinsic eye pattern characteristics from people to people, scale, pose, glasses frame, illumination, etc. To prevail from these problems, this paper addresses a novel and precise robust eye location method. It employs appearance based Bayesian framework to relive the effect of uneven illumination. The appearance of eye patterns is represented by 2D Haar wavelet. It also employs a sophisticated merging and arbitration strategy in order to manage the variations in geometrical characteristics of ambient eye regions due to glasses frames, eye brows, and so on. The located eye candidates are merged or eliminated according to the merging rule. If the merged regions are more than one, we apply the arbitration strategy. The arbitration strategy is based on a minimizing energy function by probabilistic forces and image forces that pull it toward eyes. The experimental results show that the proposed approach can achieve superior performance using various data sets to previously proposed methods.


Mahalanobis Distance Bayesian Classifier Probabilistic Force Merging Strategy Uneven Illumination 
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.


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  1. 1.
    Liu, C.: A Bayesian Discriminating Features Method for Face Detection. IEEE Trans. Pattern Analysis and Machine Intelligence 25(6), 725–740 (2003)CrossRefGoogle Scholar
  2. 2.
    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
  3. 3.
    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
  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.
    Zhou, H., Geng, X.: Projection functions for eye detection. Pattern Recognition (in press, 2004)Google Scholar
  6. 6.
    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
  7. 7.
    Ichihashi, H., Honda, K., Wakami, N.: Robust PCA with Intra-sample Outlier Process Based on Fuzzy Mahalanobis Distances and Noise Clustering. In: IEEE International Conference on Fuzzy Systems (2005)Google Scholar
  8. 8.
    Watabe, A., Komiya, K., Usuki, J., Suzuki, K., Ikeda, H.: Effective Designation of Specific Shots on Video Service System Utilizing Mahalanobis Distance. IEEE Transactions on Consumer Electronics 51(1), (February 2005)Google Scholar
  9. 9.
    Zuo, F., Real-time face recognition for smart home applications. In: Consumer Electronics, 2005. ICCE. 2005 Digest of Technical Papers. International Conference, January 8-12, 2005, pp. 35–36 (2005) Google Scholar
  10. 10.
    Gao, Y., Leung: Face recognition using line edge map. Pattern Analysis and Machine Intelligence. IEEE Transactions 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.
    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
  13. 13.
    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
  14. 14.
    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
  15. 15.
    Baskan, S., Bulut, M.M., Atalay, V.: Projection based method for segmentation of human face and its evaluation. Pattern Recognition Letters 23, 1623–1629 (2002)MATHCrossRefGoogle Scholar
  16. 16.
    Smeraldi, F., Bigun, J.: Retinal vision applied to facial features detection and face authentication. Pattern Recognition Letters 23, 463–475 (2002)MATHCrossRefGoogle Scholar
  17. 17.
    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

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Eun Jin Koh
    • 1
  • Phill Kyu Rhee
    • 1
  1. 1.Department of computer science & Engineering Inha UniversityBiometric Engineering Research Center Young-Hyun DongIncheonKorea

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