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Application of the Polar-Fourier Greyscale Descriptor to the Problem of Identification of Persons Based on Ear Images

  • Dariusz Frejlichowski
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 102)

Summary

The threat caused by criminality and terrorism resulted in a significant increase in the interest in research focused on effective methods for its reducing. One of the most important solutions for the mentioned problem is the biometric identification of persons. Hence, it became a very important issue nowadays. A crucial aspect of biometrics is the searching for automatic methods that may be applied for the recognition of human beings. Currently, fingerprints as well as the face are the most popular applied biometric features. However, in order to increase the efficiency of the developed systems, new modalities are becoming more and more popular. On one hand, researchers are looking for more effective biometrics while on the other hand the idea of applying few modalities jointly in multimodal systems is becoming more popular. An ear is an example of lately explored new biometric features. Its uniqueness is the most important advantage. Similarly to the face, the auricle is distinguishable for various persons, thanks to its complex and stable structure. Therefore, in the paper an algorithm for human identification based on ear images is presented and tested. It uses the improved version of the Polar-Fourier Greyscale Descriptor for feature representation. The method was tested using 225 digital ear images for 45 persons and has achieved 84% efficiency.

Keywords

Ear Biometrics Ear Recognition Polar-Fourier Transform 

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References

  1. 1.
    Holyst, B.: Criminalistics. LexisNexis Press, Warsaw (2004) (in Polish)Google Scholar
  2. 2.
    Kasprzak, K.: Criminalistic Otoscopy. University of Warmia and Mazury Press, Olsztyn (2003) (in Polish)Google Scholar
  3. 3.
    Burge, M., Burger, W.: Ear Biometrics for Computer Vision. In: Proc. of the 21st Workshop of the Austrian Association for Pattern Recognition, pp. 275–282 (1997)Google Scholar
  4. 4.
    Moreno, B., Sanches, A.: On the use of outer ear images for personal identification in security applications. In: Proc. of the IEEE 33rd Annual Intl. Conf. on Security Technology, pp. 469–476 (1999)Google Scholar
  5. 5.
    Hurley, D., Nixon, M., Carter, J.: Automatic Ear Recognition by Force Field Transformations. IEEE Colloquium on Visual Biometrics 7/1–7/5 (2000)Google Scholar
  6. 6.
    Hurley, D., Nixon, M., Carter, J.: Force Field Energy Functionals for Image Feature Extraction. Image and Vision Computing 20(5–6), 311–317 (2002)CrossRefGoogle Scholar
  7. 7.
    Chang, K., Bowyer, K., Sakar, S., Victor, B.: Comparison and combination of ear and face images in appearance-based biometrics. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(9), 1160–1165 (2003)CrossRefGoogle Scholar
  8. 8.
    Mu, Z., Yuan, L., Xu, Z., Xi, D., Qi, S.: Shape and structural feature based ear recognition. In: Li, S.Z., Lai, J.-H., Tan, T., Feng, G.-C., Wang, Y. (eds.) SINOBIOMETRICS 2004. LNCS, vol. 3338, pp. 663–670. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Choras, M.: Biometric Methods of Person Identification Basing on Ear Images. Biuletyn Informacyjny Techniki Komputerowe 1/2004, 59–69 (2004) (in Polish)Google Scholar
  10. 10.
    Choras, M.: Ear Biometrics — Methods of Feature Extraction Basing on Geometrical Parameters. Przeglad Elektrotechniczny 82(12), 5–10 (2006) (in Polish)Google Scholar
  11. 11.
    Yan, P., Bowyer, K.: A Fast Algorithm for ICP-Based 3D Shape Biometrics. Computer Vision and Image Understanding 107(3), 195–202 (2007)CrossRefGoogle Scholar
  12. 12.
    Yan, P., Bowyer, K., Chang, K.J.: ICP-Based Approaches for 3D Ear Recognition. In: Jain, A.K., Ratha, N.K. (eds.) Biometric Technology for Human Identification II. Proc. of the SPIE, Lecture Notes in Computer Science, vol. 5779, pp. 282–291 (2005)Google Scholar
  13. 13.
    Bhanu, B., Chen, H.: Human Ear Recognition in 3D. In: Proc. of the Workshop on Multimodal User Authentication, pp. 91–98 (2003)Google Scholar
  14. 14.
    Akkermans, T.H.M., Kevenaar, T.A.M., Schobben, D.W.E.: Acoustic Ear Recognition. In: Zhang, D., Jain, A.K. (eds.) ICB 2005. LNCS, vol. 3832, pp. 697–705. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Frejlichowski, D.: Identification of Erythrocyte Types in Greyscale MGG Images for Computer-Assisted Diagnosis. In: Vitriŕ J., Sanches, J.M., Hernández, M. (eds.) IbPRIA 2011. LNCS, vol. 6669, pp. 636–643. Springer, Heidelberg (2011)Google Scholar
  16. 16.
    Hupkens, T.M., de Clippeleir, J.: Noise and intensity invariant moments. Pattern Recognition Letters 16(4), 371–376 (1995)CrossRefGoogle Scholar
  17. 17.
    Kukharev, G.: Digital Image Processing. SUT Press (1998) (in Polish)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dariusz Frejlichowski
    • 1
  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of TechnologySzczecinPoland

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