Human Ear Recognition from Face Profile Images

  • Mohamed Abdel-Mottaleb
  • Jindan Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)


In this paper, we present a novel system for ear identification from profile images of the face. The system has two steps. In the first step, the ear is automatically detected from the profile image of the face. In the second step, the ear image is transformed to a force field, then feature points are extracted and the best match is found from a database. We propose a method based on differential geometry to extract ear feature points. We use a transformation of the ear image to make it suitable for extracting the feature points using differential geometry. During recognition, the feature points obtained from a query image are aligned and compared with those in the database using Hausdorff distance. The experimental results show that our method is effective.


Feature Point Hausdorff Distance Query Image Skin Region Biometric System 
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.
    Fleck, M., Forsyth, D., Bregler, C.: Finding Naked People. European Conference on Computer Vision 2, 592–602 (1996)Google Scholar
  2. 2.
    Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing images using the Hausdorff distance. IEEE Trans. on PAMI. 15(9), 850–863 (1993)Google Scholar
  3. 3.
    Wei, G., Li, D., Sethi, I.K.: Detection of side-view faces in color images. In: Proc. Fifth IEEE Workshop on Applications of Computer Vision, December 2000, pp. 79–84 (2000)Google Scholar
  4. 4.
    Hurley, D.J., Nixon, M.S., Carter, J.N.: Force Field Energy Functionals for Image Feature Extraction. In: Proceedings of Proc. 10th British Machine Vision Conference BMVC 1999, vol. 2, pp. 604–613 (1999)Google Scholar
  5. 5.
    Huang, R., Kumii, T.L.: Parallel algorithms for extracting ridges and ravines. In: Proceedings of the First Aizu International Symposium on Parallel Algorithms/Architecture Synthesis (March 1995)Google Scholar
  6. 6.
    Hamza, A.B., Krim, H.: A topological variational model for image singularities. In: Proc. 2002 IEEE International Conference on Image Processing (2002)Google Scholar
  7. 7.
    Han, S.P.: A globally convergent method for nonlinear programming. Journal of Optimization Theory and Applications 22, 297 (1977)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Ianarelli, A.: Ear Identification. Forensic Identification Series. Paramount Publishing, California (1989)Google Scholar
  9. 9.
    Burge, M., Burger, W.: Ear Biometrics in Computer Vision. In: The 15th International Conference of Pattern Recognition, ICPR 2000, pp. 826–830 (2000)Google Scholar
  10. 10.
    Chang, K., Bowyer, K., Barnabas, V.: Comparison and combination of ear and face images in appearance-based biometrics. IEEE Trans. Pattern Analysis and Machine Intelligence 25, 1160–1165 (2003)CrossRefGoogle Scholar
  11. 11.
    Bhanu, B., Chen, H.: Human ear recognition in 3D. In: Workshop on Multimodal User Authentication, pp. 91–98 (2003)Google Scholar
  12. 12.
    Chen, H., Bhanu, B.: Human Ear Detection from Side Face Range Images. In: International Conference of Pattern Recognition, ICPR 2004, vol. 3, pp. 574–577 (2004)Google Scholar
  13. 13.
    Hurley, D.J., Nixon, M.S., Carter, J.N.: Force field feature extraction for ear biometrics. Computer Vision and Image Understanding (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Mohamed Abdel-Mottaleb
    • 1
  • Jindan Zhou
    • 1
  1. 1.Department of Electrical & Computer EngineeringUniversity of MiamiCoral Gables

Personalised recommendations