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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  2/2019 - 9

Pupil Segmentation Using Orientation Fields, Radial Non-Maximal Suppression and Elliptic Approximation

LEE, S. See more information about LEE, S. on SCOPUS See more information about LEE, S. on IEEExplore See more information about LEE, S. on Web of Science, LEE, D. See more information about  LEE, D. on SCOPUS See more information about  LEE, D. on SCOPUS See more information about LEE, D. on Web of Science, PARK, Y. See more information about PARK, Y. on SCOPUS See more information about PARK, Y. on SCOPUS See more information about PARK, Y. on Web of Science
 
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Download PDF pdficon (2,079 KB) | Citation | Downloads: 579 | Views: 1,106

Author keywords
image edge detection, image segmentation, image texture analysis, iris recognition, pattern analysis

References keywords
iris(15), recognition(10), segmentation(7), sign(4), patt(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2019-05-31
Volume 19, Issue 2, Year 2019, On page(s): 69 - 74
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.02009
Web of Science Accession Number: 000475806300009
SCOPUS ID: 85066319709

Abstract
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This paper proposes a novel pupil segmentation method for robust iris recognition systems. The proposed method uses orientation fields to accurately detect an initial pupil center, and applies radial non-maximal suppression to remove non-pupil boundaries. Finally, we repeatedly fit the pupil boundary by radius-updating, center-shifting and region of interest (ROI) shrinking adjusting the radius and center of a circular model, and the estimated pupil boundary is approximated with a novel elliptic model. By the elliptic approximation, the pupil boundaries are more correctly segmented than those of circular models. The detection hit ratio is largely improved due to robust detection of the initial centers. The experimental results show that the proposed method can accurately detect pupils for various iris images.


References | Cited By  «-- Click to see who has cited this paper

[1] L. Ma, T. Tan, Y. Wang, and D. Zhang, "Efficient iris recognition by characterizing key local variations," IEEE. Trans. Imag. Proc., vol. 13, no. 6, pp. 739-750, 2004.
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[2] A. Jain, R. Bolle and S. Pankanti, "Biometrics: Personal Identification in a Networked Society," Springer US, New York, 2006.

[3] D. Zhang, Automated Biometrics, "Technologies and Systems," Springer US, New York, 2000.

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[CrossRef] [Web of Science Times Cited 1722] [SCOPUS Times Cited 2396]


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[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 15]


[6] H. Proenca and L.A. Alexandre, "Introduction to the special issue on the segmentation of visible wavelength iris images captured at-a-distance and on-the-move," Image Vis. Comput., vol. 28, no. 2, pp. 213-214, 2010.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 23]


[7] N. B. Puhan, N. Sudha, and A. S. Kaushalram, "Efficient segmentation technique for noisy frontal view iris images using Fourier spectral density," Sign. Imag. Video Proc., vol. 5, no. 1, pp. 105-119, 2011.
[CrossRef] [Web of Science Times Cited 33] [SCOPUS Times Cited 38]


[8] J. Huang, X. You, Y. Y. Tang, L. Du, and Y. Yuan, "A novel iris segmentation using radial-suppression edge detection," Sign. Proc., vol. 89, no. 12, pp. 2630-2643, 2009.
[CrossRef] [Web of Science Times Cited 37] [SCOPUS Times Cited 51]


[9] A. Radman, N. Zainal, ans S. A. Suandi, "Automated segmentation of iris images acquired in an unconstrained environment using HOG-SVM and GrowCut," Digit. Sign. Proc., vol. 64, pp. 60-70, 2017.
[CrossRef] [Web of Science Times Cited 44] [SCOPUS Times Cited 55]


[10] J. Daugman, "The importance of being random: statistical principles of iris recognition," Patt. Recogn., vol. 36, no. 2, pp. 279-291, 2003.
[CrossRef] [SCOPUS Times Cited 739]


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[12] J. Huang, Y. Wang, T. Tan, and J. Cui, "A new iris segmentation method for recognition," in Proc. 17th Int. Conf. Pattern Recognition, Cambridge, UK, 2004, pp. 554-557.
[CrossRef] [Web of Science Times Cited 54] [SCOPUS Times Cited 92]


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[CrossRef] [Web of Science Times Cited 261] [SCOPUS Times Cited 346]


[14] R. Krishnamoorthi and G. Annapoorani, "A simple boundary extraction technique for irregular pupil localization with orthogonal polynomials," Comp. Vis. Imag. Underst., vol. 116, no. 2, pp. 262-273, 2012.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 9]


[15] J. Koh, V.Govindaraju, and V. Chaudhary, "A robust iris localization method using an active contour model and Hough transform," in Proc. 20th Int. Conf. Pattern Recognition, Istanbul, Turkey, 2010, pp. 2852-2856.
[CrossRef] [SCOPUS Times Cited 57]


[16] S. Shah, "Iris segmentation using geodesic active contours," IEEE Trans. Inform. For. Sec., vol. 4, no. 4, pp. 824-836, 2009.
[CrossRef] [Web of Science Times Cited 162] [SCOPUS Times Cited 216]


[17] K. Miyazawa, K. Ito, T. Aoki, K. Kobayashi, and H. Nakajima, "An effective approach for iris recognition using phase-based image matching," IEEE Trans. Patt. Analys. Mach. Intell., vol. 30, no. 10, pp. 1741-1756, 2008.
[CrossRef] [Web of Science Times Cited 181] [SCOPUS Times Cited 229]


[18] Z. Z. Abidin, et al., "Iris segmentation analysis using integro-differential operator and Hough transform in biometric system," J. Telec. Electr. Comput. Eng., vol. 4, no. 2, pp. 41-48, 2012

[19] L. Hong, Y. Wan, and A. K. Jain, "Fingerprint image enhancement: algorithm and performance evaluation," IEEE Trans. Patt. Analys. Mach. Intell., vol.20, no. 8, pp. 777-789, 1998.
[CrossRef] [Web of Science Times Cited 1191] [SCOPUS Times Cited 1680]


[20] M. Liu, X. Jiang, and A. C. Kot, "Fingerprint reference-point detection," EURASIP J. Appli. Sign. Proc., vol. 5, pp. 498-509, 2005.
[CrossRef] [Web of Science Times Cited 63] [SCOPUS Times Cited 89]


[21] M. Tonsen, X. Zhang, Y. Sugano, and A. Bulling, "Labelled pupils in the wild: a dataset for studying pupil detection in unconstrained environments," in Proc. ACM Int. Symp. Eye Tracking Research & Applications, SC, USA, 2016, pp. 139-142.
[CrossRef] [Web of Science Times Cited 62] [SCOPUS Times Cited 69]




References Weight

Web of Science® Citations for all references: 4,373 TCR
SCOPUS® Citations for all references: 6,887 TCR

Web of Science® Average Citations per reference: 199 ACR
SCOPUS® Average Citations per reference: 313 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2022-09-22 22:19 in 113 seconds.




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