Abstract

Iris-based biometric systems identify individuals based on the characteristics of their iris, since they are proven to remain unique for a long time. An iris recognition system includes four phases, the most important of which is preprocessing in which the iris segmentation is performed. The accuracy of an iris biometric system critically depends on the segmentation system. In this paper, an iris segmentation system using edge detection techniques and Hough transforms is presented. The newly proposed edge detection system enhances the performance of the segmentation in a way that it performs much more efficiently than the other conventional iris segmentation methods.

© 2011 Optical Society of America

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  1. K. Jain, R. Bolle, and S. Pankanti, Biometrics: Personal Identification in Networked Society (Kluwer, 1999).
  2. S. Pankanti, R. M. Bolle, and A. Jain, “Biometrics: the future of identification,” Computer 33, 46–49 (2000).
  3. C. W. Oyster, The Human Eye Structure and Function(Sinauer, 1999).
  4. J. G. Daugman, “How iris recognition works,” IEEE Trans. Circuits Syst.Video Technol. 14, 21–30 (2004).
    [CrossRef]
  5. J. G. Daugman, “The importance of being random: statistical principles of iris recognition,” Pattern Recogn. 36, 279–291(2003).
    [CrossRef]
  6. D. S. Jeong, J. W. Hwang, B. J. Kang, K. R. Park, C. S. Won, D.-K. Park, and J. Kim, “A new iris segmentation method for non-ideal iris images,” Image Vis. Comput. 28, 254–260 (2010).
    [CrossRef]
  7. J. Huang, X. You, Y. Y. Tang, L. Du, and Y. Yuan, “A novel iris segmentation using radial-suppression edge detection,” Signal Process. 89, 2630–2643 (2009).
    [CrossRef]
  8. A. Bertillon, “La couleur de l’iris,” Rev. Sci. Instrum. 36, 65–73 (1885).
  9. L. Flom and A. Safir, “Iris recognition system,” U.S. patent 4,641,349 (3 February 1987).
  10. R. Johnston, “Can iris patterns be used to identify people?” Annual Rep. LA-12331-PR (Los Alamos National Laboratory, Chemical and Laser Sciences Division, 1992), pp. 81–86.
  11. J. G. Daugman, “Statistical richness of visual phase information: update on recognizing persons by iris patterns,” Int. J. Comput. Vis. 45, 25–38 (2001).
    [CrossRef]
  12. J. G. Daugman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 1148–1161 (1993).
    [CrossRef]
  13. R. P. Wildes, “Iris recognition: an emerging biometric technology,” Proc. IEEE 85, 1348–1363 (1997).
    [CrossRef]
  14. Z. Sun, Y. Wang, T. Tan, and J. Cui, “Improving iris recognition accuracy via cascaded classifiers,” IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 35, 435–441 (2005).
    [CrossRef]
  15. W. Boles and B. Boashash, “A human identification technique using images of the iris and wavelet transform,” IEEE Trans. Signal Process. 46, 1185–1188 (1998).
    [CrossRef]
  16. L. Ma, T. Tan, Y. Wang, and D. Zhang, “Personal identification based on iris texture analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1519–1533 (2003).
    [CrossRef]
  17. J. F. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–698(1986).
    [CrossRef] [PubMed]
  18. S. Ulam, “Some ideas and prospects in biomathematics,” Annu. Rev. Biophys. Bioeng. 1, 277–292 (1972).
    [CrossRef] [PubMed]
  19. J. von Neumann, “The general and logical theory of automata,” in Cerebral Mechanisms in Behavior – The Hixon Symposium, L.A.Jeffress, ed. (Wiley, 1951), pp. 1–31.
  20. S. Amoroso and G. Cooper, “Tessellation structures for reproduction of arbitrary patterns,” J. Comput. Syst. Sci. 5, 455–464 (1971).
    [CrossRef]
  21. L. A. Zadeh, “Fuzzy sets,” Inf. Control 8, 338–353(1965).
    [CrossRef]
  22. H. Bustince, E. Barrenechea, M. Pagola, and J. Fernandez, “Interval-valued fuzzy sets constructed from matrices: application to edge detection,” Fuzzy Sets Syst. 160, 1819–1840(2009).
    [CrossRef]
  23. F. Jacquey, F. Comby, and O. Strauss, “Fuzzy edge detection for omnidirectional images,” Fuzzy Sets Syst. 159, 1991–2010(2008).
    [CrossRef]
  24. H. R. Tizhoosh, “Fast fuzzy edge detection,” in Proceedings of the Annual Meeting of the North American Fuzzy Information Processing Society (IEEE, 2002), pp. 239–242.
  25. H. Beigy and M. R. Meybodi, “Open synchronous cellular learning automata,” Adv. Complex Syst. 10, 527–556(2007).
    [CrossRef]
  26. Center for Biometrics and Security Research, National Laboratory of Pattern Recognition, http://www.cbsr.ia.ac.cn/english/IrisDatabase.asp.
  27. H. Proença and L. A. Alexandre, “UBIRIS: a noisy iris image database,” in 13th International Conference on Image Analysis and Processing, Lecture Notes in Computer Science (Springer, 2005), pp. 970–977.

2010

D. S. Jeong, J. W. Hwang, B. J. Kang, K. R. Park, C. S. Won, D.-K. Park, and J. Kim, “A new iris segmentation method for non-ideal iris images,” Image Vis. Comput. 28, 254–260 (2010).
[CrossRef]

2009

J. Huang, X. You, Y. Y. Tang, L. Du, and Y. Yuan, “A novel iris segmentation using radial-suppression edge detection,” Signal Process. 89, 2630–2643 (2009).
[CrossRef]

H. Bustince, E. Barrenechea, M. Pagola, and J. Fernandez, “Interval-valued fuzzy sets constructed from matrices: application to edge detection,” Fuzzy Sets Syst. 160, 1819–1840(2009).
[CrossRef]

2008

F. Jacquey, F. Comby, and O. Strauss, “Fuzzy edge detection for omnidirectional images,” Fuzzy Sets Syst. 159, 1991–2010(2008).
[CrossRef]

2007

H. Beigy and M. R. Meybodi, “Open synchronous cellular learning automata,” Adv. Complex Syst. 10, 527–556(2007).
[CrossRef]

2005

Z. Sun, Y. Wang, T. Tan, and J. Cui, “Improving iris recognition accuracy via cascaded classifiers,” IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 35, 435–441 (2005).
[CrossRef]

2004

J. G. Daugman, “How iris recognition works,” IEEE Trans. Circuits Syst.Video Technol. 14, 21–30 (2004).
[CrossRef]

2003

J. G. Daugman, “The importance of being random: statistical principles of iris recognition,” Pattern Recogn. 36, 279–291(2003).
[CrossRef]

L. Ma, T. Tan, Y. Wang, and D. Zhang, “Personal identification based on iris texture analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1519–1533 (2003).
[CrossRef]

2001

J. G. Daugman, “Statistical richness of visual phase information: update on recognizing persons by iris patterns,” Int. J. Comput. Vis. 45, 25–38 (2001).
[CrossRef]

2000

S. Pankanti, R. M. Bolle, and A. Jain, “Biometrics: the future of identification,” Computer 33, 46–49 (2000).

1998

W. Boles and B. Boashash, “A human identification technique using images of the iris and wavelet transform,” IEEE Trans. Signal Process. 46, 1185–1188 (1998).
[CrossRef]

1997

R. P. Wildes, “Iris recognition: an emerging biometric technology,” Proc. IEEE 85, 1348–1363 (1997).
[CrossRef]

1993

J. G. Daugman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 1148–1161 (1993).
[CrossRef]

1986

J. F. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–698(1986).
[CrossRef] [PubMed]

1972

S. Ulam, “Some ideas and prospects in biomathematics,” Annu. Rev. Biophys. Bioeng. 1, 277–292 (1972).
[CrossRef] [PubMed]

1971

S. Amoroso and G. Cooper, “Tessellation structures for reproduction of arbitrary patterns,” J. Comput. Syst. Sci. 5, 455–464 (1971).
[CrossRef]

1965

L. A. Zadeh, “Fuzzy sets,” Inf. Control 8, 338–353(1965).
[CrossRef]

1885

A. Bertillon, “La couleur de l’iris,” Rev. Sci. Instrum. 36, 65–73 (1885).

Alexandre, L. A.

H. Proença and L. A. Alexandre, “UBIRIS: a noisy iris image database,” in 13th International Conference on Image Analysis and Processing, Lecture Notes in Computer Science (Springer, 2005), pp. 970–977.

Amoroso, S.

S. Amoroso and G. Cooper, “Tessellation structures for reproduction of arbitrary patterns,” J. Comput. Syst. Sci. 5, 455–464 (1971).
[CrossRef]

Barrenechea, E.

H. Bustince, E. Barrenechea, M. Pagola, and J. Fernandez, “Interval-valued fuzzy sets constructed from matrices: application to edge detection,” Fuzzy Sets Syst. 160, 1819–1840(2009).
[CrossRef]

Beigy, H.

H. Beigy and M. R. Meybodi, “Open synchronous cellular learning automata,” Adv. Complex Syst. 10, 527–556(2007).
[CrossRef]

Bertillon, A.

A. Bertillon, “La couleur de l’iris,” Rev. Sci. Instrum. 36, 65–73 (1885).

Boashash, B.

W. Boles and B. Boashash, “A human identification technique using images of the iris and wavelet transform,” IEEE Trans. Signal Process. 46, 1185–1188 (1998).
[CrossRef]

Boles, W.

W. Boles and B. Boashash, “A human identification technique using images of the iris and wavelet transform,” IEEE Trans. Signal Process. 46, 1185–1188 (1998).
[CrossRef]

Bolle, R.

K. Jain, R. Bolle, and S. Pankanti, Biometrics: Personal Identification in Networked Society (Kluwer, 1999).

Bolle, R. M.

S. Pankanti, R. M. Bolle, and A. Jain, “Biometrics: the future of identification,” Computer 33, 46–49 (2000).

Bustince, H.

H. Bustince, E. Barrenechea, M. Pagola, and J. Fernandez, “Interval-valued fuzzy sets constructed from matrices: application to edge detection,” Fuzzy Sets Syst. 160, 1819–1840(2009).
[CrossRef]

Canny, J. F.

J. F. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–698(1986).
[CrossRef] [PubMed]

Comby, F.

F. Jacquey, F. Comby, and O. Strauss, “Fuzzy edge detection for omnidirectional images,” Fuzzy Sets Syst. 159, 1991–2010(2008).
[CrossRef]

Cooper, G.

S. Amoroso and G. Cooper, “Tessellation structures for reproduction of arbitrary patterns,” J. Comput. Syst. Sci. 5, 455–464 (1971).
[CrossRef]

Cui, J.

Z. Sun, Y. Wang, T. Tan, and J. Cui, “Improving iris recognition accuracy via cascaded classifiers,” IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 35, 435–441 (2005).
[CrossRef]

Daugman, J. G.

J. G. Daugman, “How iris recognition works,” IEEE Trans. Circuits Syst.Video Technol. 14, 21–30 (2004).
[CrossRef]

J. G. Daugman, “The importance of being random: statistical principles of iris recognition,” Pattern Recogn. 36, 279–291(2003).
[CrossRef]

J. G. Daugman, “Statistical richness of visual phase information: update on recognizing persons by iris patterns,” Int. J. Comput. Vis. 45, 25–38 (2001).
[CrossRef]

J. G. Daugman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 1148–1161 (1993).
[CrossRef]

Du, L.

J. Huang, X. You, Y. Y. Tang, L. Du, and Y. Yuan, “A novel iris segmentation using radial-suppression edge detection,” Signal Process. 89, 2630–2643 (2009).
[CrossRef]

Fernandez, J.

H. Bustince, E. Barrenechea, M. Pagola, and J. Fernandez, “Interval-valued fuzzy sets constructed from matrices: application to edge detection,” Fuzzy Sets Syst. 160, 1819–1840(2009).
[CrossRef]

Flom, L.

L. Flom and A. Safir, “Iris recognition system,” U.S. patent 4,641,349 (3 February 1987).

Huang, J.

J. Huang, X. You, Y. Y. Tang, L. Du, and Y. Yuan, “A novel iris segmentation using radial-suppression edge detection,” Signal Process. 89, 2630–2643 (2009).
[CrossRef]

Hwang, J. W.

D. S. Jeong, J. W. Hwang, B. J. Kang, K. R. Park, C. S. Won, D.-K. Park, and J. Kim, “A new iris segmentation method for non-ideal iris images,” Image Vis. Comput. 28, 254–260 (2010).
[CrossRef]

Jacquey, F.

F. Jacquey, F. Comby, and O. Strauss, “Fuzzy edge detection for omnidirectional images,” Fuzzy Sets Syst. 159, 1991–2010(2008).
[CrossRef]

Jain, A.

S. Pankanti, R. M. Bolle, and A. Jain, “Biometrics: the future of identification,” Computer 33, 46–49 (2000).

Jain, K.

K. Jain, R. Bolle, and S. Pankanti, Biometrics: Personal Identification in Networked Society (Kluwer, 1999).

Jeong, D. S.

D. S. Jeong, J. W. Hwang, B. J. Kang, K. R. Park, C. S. Won, D.-K. Park, and J. Kim, “A new iris segmentation method for non-ideal iris images,” Image Vis. Comput. 28, 254–260 (2010).
[CrossRef]

Johnston, R.

R. Johnston, “Can iris patterns be used to identify people?” Annual Rep. LA-12331-PR (Los Alamos National Laboratory, Chemical and Laser Sciences Division, 1992), pp. 81–86.

Kang, B. J.

D. S. Jeong, J. W. Hwang, B. J. Kang, K. R. Park, C. S. Won, D.-K. Park, and J. Kim, “A new iris segmentation method for non-ideal iris images,” Image Vis. Comput. 28, 254–260 (2010).
[CrossRef]

Kim, J.

D. S. Jeong, J. W. Hwang, B. J. Kang, K. R. Park, C. S. Won, D.-K. Park, and J. Kim, “A new iris segmentation method for non-ideal iris images,” Image Vis. Comput. 28, 254–260 (2010).
[CrossRef]

Ma, L.

L. Ma, T. Tan, Y. Wang, and D. Zhang, “Personal identification based on iris texture analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1519–1533 (2003).
[CrossRef]

Meybodi, M. R.

H. Beigy and M. R. Meybodi, “Open synchronous cellular learning automata,” Adv. Complex Syst. 10, 527–556(2007).
[CrossRef]

Oyster, C. W.

C. W. Oyster, The Human Eye Structure and Function(Sinauer, 1999).

Pagola, M.

H. Bustince, E. Barrenechea, M. Pagola, and J. Fernandez, “Interval-valued fuzzy sets constructed from matrices: application to edge detection,” Fuzzy Sets Syst. 160, 1819–1840(2009).
[CrossRef]

Pankanti, S.

S. Pankanti, R. M. Bolle, and A. Jain, “Biometrics: the future of identification,” Computer 33, 46–49 (2000).

K. Jain, R. Bolle, and S. Pankanti, Biometrics: Personal Identification in Networked Society (Kluwer, 1999).

Park, D.-K.

D. S. Jeong, J. W. Hwang, B. J. Kang, K. R. Park, C. S. Won, D.-K. Park, and J. Kim, “A new iris segmentation method for non-ideal iris images,” Image Vis. Comput. 28, 254–260 (2010).
[CrossRef]

Park, K. R.

D. S. Jeong, J. W. Hwang, B. J. Kang, K. R. Park, C. S. Won, D.-K. Park, and J. Kim, “A new iris segmentation method for non-ideal iris images,” Image Vis. Comput. 28, 254–260 (2010).
[CrossRef]

Proença, H.

H. Proença and L. A. Alexandre, “UBIRIS: a noisy iris image database,” in 13th International Conference on Image Analysis and Processing, Lecture Notes in Computer Science (Springer, 2005), pp. 970–977.

Safir, A.

L. Flom and A. Safir, “Iris recognition system,” U.S. patent 4,641,349 (3 February 1987).

Strauss, O.

F. Jacquey, F. Comby, and O. Strauss, “Fuzzy edge detection for omnidirectional images,” Fuzzy Sets Syst. 159, 1991–2010(2008).
[CrossRef]

Sun, Z.

Z. Sun, Y. Wang, T. Tan, and J. Cui, “Improving iris recognition accuracy via cascaded classifiers,” IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 35, 435–441 (2005).
[CrossRef]

Tan, T.

Z. Sun, Y. Wang, T. Tan, and J. Cui, “Improving iris recognition accuracy via cascaded classifiers,” IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 35, 435–441 (2005).
[CrossRef]

L. Ma, T. Tan, Y. Wang, and D. Zhang, “Personal identification based on iris texture analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1519–1533 (2003).
[CrossRef]

Tang, Y. Y.

J. Huang, X. You, Y. Y. Tang, L. Du, and Y. Yuan, “A novel iris segmentation using radial-suppression edge detection,” Signal Process. 89, 2630–2643 (2009).
[CrossRef]

Tizhoosh, H. R.

H. R. Tizhoosh, “Fast fuzzy edge detection,” in Proceedings of the Annual Meeting of the North American Fuzzy Information Processing Society (IEEE, 2002), pp. 239–242.

Ulam, S.

S. Ulam, “Some ideas and prospects in biomathematics,” Annu. Rev. Biophys. Bioeng. 1, 277–292 (1972).
[CrossRef] [PubMed]

von Neumann, J.

J. von Neumann, “The general and logical theory of automata,” in Cerebral Mechanisms in Behavior – The Hixon Symposium, L.A.Jeffress, ed. (Wiley, 1951), pp. 1–31.

Wang, Y.

Z. Sun, Y. Wang, T. Tan, and J. Cui, “Improving iris recognition accuracy via cascaded classifiers,” IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 35, 435–441 (2005).
[CrossRef]

L. Ma, T. Tan, Y. Wang, and D. Zhang, “Personal identification based on iris texture analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1519–1533 (2003).
[CrossRef]

Wildes, R. P.

R. P. Wildes, “Iris recognition: an emerging biometric technology,” Proc. IEEE 85, 1348–1363 (1997).
[CrossRef]

Won, C. S.

D. S. Jeong, J. W. Hwang, B. J. Kang, K. R. Park, C. S. Won, D.-K. Park, and J. Kim, “A new iris segmentation method for non-ideal iris images,” Image Vis. Comput. 28, 254–260 (2010).
[CrossRef]

You, X.

J. Huang, X. You, Y. Y. Tang, L. Du, and Y. Yuan, “A novel iris segmentation using radial-suppression edge detection,” Signal Process. 89, 2630–2643 (2009).
[CrossRef]

Yuan, Y.

J. Huang, X. You, Y. Y. Tang, L. Du, and Y. Yuan, “A novel iris segmentation using radial-suppression edge detection,” Signal Process. 89, 2630–2643 (2009).
[CrossRef]

Zadeh, L. A.

L. A. Zadeh, “Fuzzy sets,” Inf. Control 8, 338–353(1965).
[CrossRef]

Zhang, D.

L. Ma, T. Tan, Y. Wang, and D. Zhang, “Personal identification based on iris texture analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1519–1533 (2003).
[CrossRef]

Adv. Complex Syst.

H. Beigy and M. R. Meybodi, “Open synchronous cellular learning automata,” Adv. Complex Syst. 10, 527–556(2007).
[CrossRef]

Annu. Rev. Biophys. Bioeng.

S. Ulam, “Some ideas and prospects in biomathematics,” Annu. Rev. Biophys. Bioeng. 1, 277–292 (1972).
[CrossRef] [PubMed]

Computer

S. Pankanti, R. M. Bolle, and A. Jain, “Biometrics: the future of identification,” Computer 33, 46–49 (2000).

Fuzzy Sets Syst.

H. Bustince, E. Barrenechea, M. Pagola, and J. Fernandez, “Interval-valued fuzzy sets constructed from matrices: application to edge detection,” Fuzzy Sets Syst. 160, 1819–1840(2009).
[CrossRef]

F. Jacquey, F. Comby, and O. Strauss, “Fuzzy edge detection for omnidirectional images,” Fuzzy Sets Syst. 159, 1991–2010(2008).
[CrossRef]

IEEE Trans. Circuits Syst.Video Technol.

J. G. Daugman, “How iris recognition works,” IEEE Trans. Circuits Syst.Video Technol. 14, 21–30 (2004).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell.

J. G. Daugman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 1148–1161 (1993).
[CrossRef]

L. Ma, T. Tan, Y. Wang, and D. Zhang, “Personal identification based on iris texture analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1519–1533 (2003).
[CrossRef]

J. F. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–698(1986).
[CrossRef] [PubMed]

IEEE Trans. Signal Process.

W. Boles and B. Boashash, “A human identification technique using images of the iris and wavelet transform,” IEEE Trans. Signal Process. 46, 1185–1188 (1998).
[CrossRef]

IEEE Trans. Syst. Man Cybern. Part C Appl. Rev.

Z. Sun, Y. Wang, T. Tan, and J. Cui, “Improving iris recognition accuracy via cascaded classifiers,” IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 35, 435–441 (2005).
[CrossRef]

Image Vis. Comput.

D. S. Jeong, J. W. Hwang, B. J. Kang, K. R. Park, C. S. Won, D.-K. Park, and J. Kim, “A new iris segmentation method for non-ideal iris images,” Image Vis. Comput. 28, 254–260 (2010).
[CrossRef]

Inf. Control

L. A. Zadeh, “Fuzzy sets,” Inf. Control 8, 338–353(1965).
[CrossRef]

Int. J. Comput. Vis.

J. G. Daugman, “Statistical richness of visual phase information: update on recognizing persons by iris patterns,” Int. J. Comput. Vis. 45, 25–38 (2001).
[CrossRef]

J. Comput. Syst. Sci.

S. Amoroso and G. Cooper, “Tessellation structures for reproduction of arbitrary patterns,” J. Comput. Syst. Sci. 5, 455–464 (1971).
[CrossRef]

Pattern Recogn.

J. G. Daugman, “The importance of being random: statistical principles of iris recognition,” Pattern Recogn. 36, 279–291(2003).
[CrossRef]

Proc. IEEE

R. P. Wildes, “Iris recognition: an emerging biometric technology,” Proc. IEEE 85, 1348–1363 (1997).
[CrossRef]

Rev. Sci. Instrum.

A. Bertillon, “La couleur de l’iris,” Rev. Sci. Instrum. 36, 65–73 (1885).

Signal Process.

J. Huang, X. You, Y. Y. Tang, L. Du, and Y. Yuan, “A novel iris segmentation using radial-suppression edge detection,” Signal Process. 89, 2630–2643 (2009).
[CrossRef]

Other

L. Flom and A. Safir, “Iris recognition system,” U.S. patent 4,641,349 (3 February 1987).

R. Johnston, “Can iris patterns be used to identify people?” Annual Rep. LA-12331-PR (Los Alamos National Laboratory, Chemical and Laser Sciences Division, 1992), pp. 81–86.

C. W. Oyster, The Human Eye Structure and Function(Sinauer, 1999).

K. Jain, R. Bolle, and S. Pankanti, Biometrics: Personal Identification in Networked Society (Kluwer, 1999).

J. von Neumann, “The general and logical theory of automata,” in Cerebral Mechanisms in Behavior – The Hixon Symposium, L.A.Jeffress, ed. (Wiley, 1951), pp. 1–31.

H. R. Tizhoosh, “Fast fuzzy edge detection,” in Proceedings of the Annual Meeting of the North American Fuzzy Information Processing Society (IEEE, 2002), pp. 239–242.

Center for Biometrics and Security Research, National Laboratory of Pattern Recognition, http://www.cbsr.ia.ac.cn/english/IrisDatabase.asp.

H. Proença and L. A. Alexandre, “UBIRIS: a noisy iris image database,” in 13th International Conference on Image Analysis and Processing, Lecture Notes in Computer Science (Springer, 2005), pp. 970–977.

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Figures (11)

Fig. 1
Fig. 1

Features of an eye.

Fig. 2
Fig. 2

Performance of the fuzzy preprocessing stage of the edge detector.

Fig. 3
Fig. 3

Interaction between the probabilistic environment and LA.

Fig. 4
Fig. 4

Different types of neighborhood.

Fig. 5
Fig. 5

Penalty patterns. (a) Thick edge. (b) Noises. (c) Unwanted edges.

Fig. 6
Fig. 6

Comparison between the products of (a) the fuzzy preprocessing stage and (b) the CLA enhancement stage .

Fig. 7
Fig. 7

Framework of the proposed edge detector.

Fig. 8
Fig. 8

General framework of the proposed iris segmentation approach.

Fig. 9
Fig. 9

Noncircular irides that could not be detected accurately using the segmentation method based on (a) the Sobel edge detector, (b) the Canny edge detector, and (c) the proposed edge detector.

Fig. 10
Fig. 10

(a) Original image, (b) image obtained from the Sobel-based segmentation method, (c) image obtained from the Canny-based segmentation method, and (d) image obtained from the proposed segmentation method.

Fig. 11
Fig. 11

(a) Original image, (b) image obtained from the Sobel-based segmentation method, (c) image obtained from the Canny-based segmentation method, and (d) image obtained from the proposed segmentation method.

Tables (1)

Tables Icon

Table 1 Iris Detection Accuracy Rate for the Aforementioned Methods

Equations (7)

Equations on this page are rendered with MathJax. Learn more.

max ( r , x 0 , y 0 ) | G σ ( r ) × r r , x 0 , y 0 I ( x , y ) 2 π r d s | ,
X = m = 1 M n = 1 N μ m n g m n .
μ ͡ m n 1 = min ( 1 , max W ( i , j ) i , j [ 1 , w ] min W ( i , j ) i , j [ 1 , w ] Δ 1 ) ,
μ ͡ m n 2 = 1 min ( 1 , g m n MeanMed   W ( i , j ) i , j [ 1 , w ] Δ 2 ) ,
X = m = 1 M n = 1 N min ( μ ͡ m n 1 , μ ͡ m n 2 ) g m n .
ρ ( n + 1 ) = ( 1 β ) ρ ( n ) ρ ( n + 1 ) = β ( 255 ρ ( n ) ) ,
ρ ( n + 1 ) = ρ ( n ) + α ( 255 ρ ( n ) ) ρ ( n + 1 ) = ( 1 + α ) ρ ( n ) ,

Metrics