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Finger-Knuckle Print Recognition Using Gradient Orientation Feature

그레이디언트 방향 특징을 이용한 손가락 관절문 인식

  • 김민기 (경상대학교 컴퓨터과학과/컴퓨터정보통신연구소)
  • Received : 2012.09.25
  • Accepted : 2012.11.01
  • Published : 2012.12.28

Abstract

Biometrics is a study of identifying individual by using the features of human body. It has been studied for an alternative or complementary method for the classical method based on password, ID card, etc. In comparison with the fingerprint, iris, ear, palmprint, finger-knuckle print has been recently studied. This paper proposes an effective method for recognizing finger-knuckle print based on the feature of Gradient orientation. The main features of finger-knuckle print are the size and direction of winkles. In order to extract these features stably, we make a feature vector consisted of Gradient orientations after the preprocessing of enhancing non-uniform brightness and low contrast. Total 790 images acquired from 158 persons have been used at the experiment for evaluating the performance of the proposed method. The experimental results show the recognition rate of 99.69% and the relatively high decidability index of 1.882. These results demonstrate that the proposed method is effective in recognizing finger-knuckle print.

Keywords

Biometrics;Finger-Knuckle Print;Gradient Orientation

References

  1. N. K. Ratha, A. Senior, and R. M. Bolle, "Automated biometrics," ICAPR 2001, LNCS Vol.2013, pp.445-453, 2001.
  2. S. Shah and A. Ross, "Iris Segmentation Using Geodesic Active Contours," IEEE Trans. on Information Forensics and Security, Vol.4, No.4, pp.824-836, 2009. https://doi.org/10.1109/TIFS.2009.2033225
  3. L. Yuan and Z. Mu, "Ear recognition based on local information fusion," Pattern Recognition Letters, Vol.33, pp.182-190, 2012. https://doi.org/10.1016/j.patrec.2011.09.041
  4. L. Zhang, L. Zhang, and D. Zhang, "Finger -knuckle-print: A new biometric identifier," 16th Infernational Conf. on Image Processing(ICIP), pp.1981-1984, 2009.
  5. A. Kumar and Y. Zhou, "Human Identification Using KnuckleCodes," BTAS '09, pp.1-6, 2009.
  6. L. Zhang, L. Zhang, D. Zhang, and H. Zhu, "Online finger-knuckle-print verification for personal authentication," Pattern Recognition, Vol.43, pp.2560-2571, 2010. https://doi.org/10.1016/j.patcog.2010.01.020
  7. A. Kumar and C. Ravikanth, "Personal Authen tication Using Finger Knuckle Surface," EEE Trans. on Information Forensics and Security, Vol.4, No.1, pp.98-110, 2009. https://doi.org/10.1109/TIFS.2008.2011089
  8. C. Ravikanth and Kumar, "Biometric Authentication Using Finger-Back Surface," Interna tional Conf. on CVPR'07, pp.1-6, 2007.
  9. H. B. Kekre and V. A. Bharadi, "Finger-Knuckle-Print Verification using Kekre's Wavelet Transform," ICWET 2011, pp.32-37, 2011.
  10. F. Bianconi and A. Fernandez, "Evaluation of the effects of Gabor filter parameters on texture classification," Pattern Recognition, Vol.40, pp.3325-3335, 2007. https://doi.org/10.1016/j.patcog.2007.04.023
  11. C. Liu, "Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition," IEEE Trans. on PAMI, Vol.26, No.5, pp.572-581, 2004. https://doi.org/10.1109/TPAMI.2004.1273927
  12. W. K. Kong, D. Zhang, and W. Li, "Palmprint feature extraction using 2-D Gaobr filters," Pattern Recognition, Vol.36, pp.2339-2347, 2003. https://doi.org/10.1016/S0031-3203(03)00121-3
  13. A. Kumar and Y. Zhou, "Personal identification using finger knuckle orientation features," Electronics Letters, Vol.45, No.20, pp.1023-1025, 2009. https://doi.org/10.1049/el.2009.1435
  14. R. Kirsch, "Computer determination of the constituent structure of biological images," Computers & Biomedical Research, Vol.4, pp.315-328, 1971. https://doi.org/10.1016/0010-4809(71)90034-6
  15. L. Huang, A. Shimizu, Y. Hagihara, and H. Kobatake, "Gradient feature extraction for classification-based face detection," Pattern Recognition, Vol.36, pp.2501-2511, 2003. https://doi.org/10.1016/S0031-3203(03)00130-4
  16. http://www4.comp.polyu.edu.hk/-csajaykr/IITD/iitd_knuckle.htm
  17. J. Daugman, "The importance of being random: statistical principles of iris recognition," Vol.36, pp.279-291, 2003. https://doi.org/10.1016/S0031-3203(02)00030-4