International Journal of Fuzzy Logic and Intelligent Systems

  • 제3권2호
  • /
  • Pages.178-186
  • /
  • 2003
  • /
  • 1598-2645(pISSN)
  • /
  • 2093-744X(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
권호 표지
DOI QR코드

DOI QR Code

Human Iris Recognition using Wavelet Transform and Neural Network

  • Cho, Seong-Won (School of Electronics and Electrical Engineering, Hongik University) ;
  • Kim, Jae-Min (School of Electronics and Electrical Engineering, Hongik University) ;
  • Won, Jung-Woo (School of Electronics and Electrical Engineering, Hongik University)
  • 발행 : 2003.12.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Recently, many researchers have been interested in biometric systems such as fingerprint, handwriting, key-stroke patterns and human iris. From the viewpoint of reliability and robustness, iris recognition is the most attractive biometric system. Moreover, the iris recognition system is a comfortable biometric system, since the video image of an eye can be taken at a distance. In this paper, we discuss human iris recognition, which is based on accurate iris localization, robust feature extraction, and Neural Network classification. The iris region is accurately localized in the eye image using a multiresolution active snake model. For the feature representation, the localized iris image is decomposed using wavelet transform based on dyadic Haar wavelet. Experimental results show the usefulness of wavelet transform in comparison to conventional Gabor transform. In addition, we present a new method for setting initial weight vectors in competitive learning. The proposed initialization method yields better accuracy than the conventional method.

키워드

참고문헌

  1. D. H. Zhang, Automated biometrics: Technologies and Systems, Kluwer Academic Publishers, 2000
  2. F. H. Adler, Physiology of the Eye: Ctmicat AppUcation, The C. V. Mosby Company, 1965
  3. P. W. Hallinan, 'Recognizing Human Eyes', SPIE Proc. of Geometric Methods in Computer Vision, Vol. 1570, pp. 214-226, 1991 https://doi.org/10.1117/12.48426
  4. John G. Daugman, 'High Confidence Visual Recognition of Persons by a Test of Statistical Independence', IEEE Transaction on Pattem Analysis and Machine Intelligence, Vol. 15, No. 11, pp. 1148-1161, 1993 https://doi.org/10.1109/34.244676
  5. Richard P. Wildes, 'Iris Recognition: An Emerging Biometric Technology', Proceedings of the IEEE, Vol. 85, No. 9, pp. 1348-1363, 1997 https://doi.org/10.1109/5.628669
  6. W. W. Boles and B. Boashash, 'A Human Identification Technique Using images of the Iris and Wavelet Transform', IEEE Transaction on Signal Processing, Vol. 46, No. 4, pp. 1185-1188, 1998 https://doi.org/10.1109/78.668573
  7. D. de Martin-Roche, C. Sanchez-Avila, and R. Sanchez-Reillo, 'Iris Recognition for Biometric Identifica-tion using Dyadic Wavelet Transform Zero-Crossing,' Security Technology, 2001 IEEE 35th International Camahan Conference, pp. 272-277, 2001
  8. Gerold O. Williams, 'Iris Recognition Technology', IEEE AES Systems Magazine 1997, pp. 23-29, April 1997
  9. W. K. Kong and D. Zhang, 'Accurate iris segmentation based on novel reflection and eyelash detection model,' Proceeding of 2001 International Symposium on Intelligent, Multimedia, Video and Speech Processing, May 2001, Hong Kong
  10. M. K.ass, A. Witkin, and D. Terzopoulos, 'Sake: Active contour models,' in Proceedings of First International Conference on Computer Vision, pp. 259-269, London, 1987
  11. D. J. Williams and M. Shah, 'A fast algorithm for active contours and curvature estimation,' CVGIP: Image Understanding, Vol. 55, No. 1, January, pp.14-26, 1992 https://doi.org/10.1016/1049-9660(92)90003-L
  12. Gilbert Strang, Truong Nguyen, Wavelets and Filter Banks, WeIlesley-Cambridge Press, 1996
  13. L. Fausset, Fundamentals of Neural Networks, Prentice Hall, 1994
  14. T. Kohonen, The Setf-organization and Associate Memory, Springer-Verlag, 1985
  15. S. Cho and H. Seong, Iris recognition using Gabor transform and neural net , Journal of Fuzzy Logic and Intelligent Systems, Vol. 7, No. 2, pp. 397i 401, 1997