Iris Localization using Circle and Fuzzy Circle Detection Method
Iris localization is a very important approach in
biometric identification systems. Identification process usually is
implemented in three levels: iris localization, feature extraction, and
pattern matching finally. Accuracy of iris localization as the first step
affects all other levels and this shows the importance of iris
localization in an iris based biometric system. In this paper, we
consider Daugman iris localization method as a standard method,
propose a new method in this field and then analyze and compare the
results of them on a standard set of iris images. The proposed method
is based on the detection of circular edge of iris, and improved by
fuzzy circles and surface energy difference contexts. Implementation
of this method is so easy and compared to the other methods, have a
rather high accuracy and speed. Test results show that the accuracy of
our proposed method is about Daugman method and computation
speed of it is 10 times faster.
[1] Chiara. Braghin, "Biometric Authentication", Technical report, 2000,
http://citeseer.ist.psu.edu/436492.html.
[2] NSTC Subcommittee, "Iris Recognition", Aug. 2006,
http://www.biometricscatalog.org/NSTCSubcommittee.
[3] J. Daugman, "How iris recognition works", IEEE Trans. Circuits Syst.
Video Technol., vol. 14, no. 1, pp. 21-30, Jan. 2004.
[4] J. Daugman, "High Confidence Visual Recognition of Person by Test of
Statistical Independence", IEEE Trans. Pattern Analysis. Machine
Intel., vol. 15, no. 11, pp. 1148-1161, Nov. 1993.
[5] Chinese Academy of Sciences- Institute of Automation (CASIA),
CASIA-IrisV3 Iris Database, http://www.cbsr.ia.ac.cn/IrisDatabase.htm.
[1] Chiara. Braghin, "Biometric Authentication", Technical report, 2000,
http://citeseer.ist.psu.edu/436492.html.
[2] NSTC Subcommittee, "Iris Recognition", Aug. 2006,
http://www.biometricscatalog.org/NSTCSubcommittee.
[3] J. Daugman, "How iris recognition works", IEEE Trans. Circuits Syst.
Video Technol., vol. 14, no. 1, pp. 21-30, Jan. 2004.
[4] J. Daugman, "High Confidence Visual Recognition of Person by Test of
Statistical Independence", IEEE Trans. Pattern Analysis. Machine
Intel., vol. 15, no. 11, pp. 1148-1161, Nov. 1993.
[5] Chinese Academy of Sciences- Institute of Automation (CASIA),
CASIA-IrisV3 Iris Database, http://www.cbsr.ia.ac.cn/IrisDatabase.htm.
@article{"International Journal of Information, Control and Computer Sciences:50608", author = "Marzieh. Savoj and S. Amirhassan. Monadjemi", title = "Iris Localization using Circle and Fuzzy Circle Detection Method", abstract = "Iris localization is a very important approach in
biometric identification systems. Identification process usually is
implemented in three levels: iris localization, feature extraction, and
pattern matching finally. Accuracy of iris localization as the first step
affects all other levels and this shows the importance of iris
localization in an iris based biometric system. In this paper, we
consider Daugman iris localization method as a standard method,
propose a new method in this field and then analyze and compare the
results of them on a standard set of iris images. The proposed method
is based on the detection of circular edge of iris, and improved by
fuzzy circles and surface energy difference contexts. Implementation
of this method is so easy and compared to the other methods, have a
rather high accuracy and speed. Test results show that the accuracy of
our proposed method is about Daugman method and computation
speed of it is 10 times faster.", keywords = "Convolution, Edge detector filter, Fuzzy circle,
Identification", volume = "6", number = "1", pages = "20-3", }