A fast circular edge detector for the iris region segmentation

Yeunggyu Park; Hoonju Yun; Myongseop Song; Jaihie Kim

doi:10.1007/3-540-45482-9_42

A fast circular edge detector for the iris region segmentation

Yeunggyu Park, Hoonju Yun, Myongseop Song, Jaihie Kim

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Citations (Scopus)

Abstract

In this paper, we propose a fast circular edge detector for the iris region segmentation by detecting the inner and outer boundaries of the iris. In previous work, the circular edge detector which John G.Daugman proposed, searches the radius and the center of the iris to detect its outer boundary over an eye image. To do so, he used Gaussian filter to smooth texture patterns of the iris which cause its outer boundary to be detected incorrectly. Gaussian filtering requires much computation, especially when the filter size increases, so it takes much time to segment the iris region. In our algorithm, we could avoid procedure for Gaussian filtering by searching the radius and the center position of the iris from a position being independent of its texture patterns. In experimental results, the proposed algorithm is compared with the previous ones, the circular edge detector with Gaussian filter and the Sobel edge detector for the eye images having different pupil and iris center positions.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Tomaso Poggio, Seong-Whan Lee, Heinrich H. Bulthoff
Publisher	Springer Verlag
Pages	417-423
Number of pages	7
ISBN (Print)	3540675604, 9783540675600
DOIs	https://doi.org/10.1007/3-540-45482-9_42
Publication status	Published - 2000
Event	1st IEEE International Workshop on Biologically Motivated Computer Vision, BMCV 2000 - Seoul, Korea, Republic of Duration: 2000 May 15 → 2000 May 17

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1811
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	1st IEEE International Workshop on Biologically Motivated Computer Vision, BMCV 2000
Country/Territory	Korea, Republic of
City	Seoul
Period	00/5/15 → 00/5/17

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2000.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/3-540-45482-9_42

Cite this

Park, Y., Yun, H., Song, M., & Kim, J. (2000). A fast circular edge detector for the iris region segmentation. In T. Poggio, S-W. Lee, & H. H. Bulthoff (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 417-423). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1811). Springer Verlag. https://doi.org/10.1007/3-540-45482-9_42

Park, Yeunggyu ; Yun, Hoonju ; Song, Myongseop et al. / A fast circular edge detector for the iris region segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Tomaso Poggio ; Seong-Whan Lee ; Heinrich H. Bulthoff. Springer Verlag, 2000. pp. 417-423 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In this paper, we propose a fast circular edge detector for the iris region segmentation by detecting the inner and outer boundaries of the iris. In previous work, the circular edge detector which John G.Daugman proposed, searches the radius and the center of the iris to detect its outer boundary over an eye image. To do so, he used Gaussian filter to smooth texture patterns of the iris which cause its outer boundary to be detected incorrectly. Gaussian filtering requires much computation, especially when the filter size increases, so it takes much time to segment the iris region. In our algorithm, we could avoid procedure for Gaussian filtering by searching the radius and the center position of the iris from a position being independent of its texture patterns. In experimental results, the proposed algorithm is compared with the previous ones, the circular edge detector with Gaussian filter and the Sobel edge detector for the eye images having different pupil and iris center positions.",

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Park, Y, Yun, H, Song, M & Kim, J 2000, A fast circular edge detector for the iris region segmentation. in T Poggio, S-W Lee & HH Bulthoff (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1811, Springer Verlag, pp. 417-423, 1st IEEE International Workshop on Biologically Motivated Computer Vision, BMCV 2000, Seoul, Korea, Republic of, 00/5/15. https://doi.org/10.1007/3-540-45482-9_42

A fast circular edge detector for the iris region segmentation. / Park, Yeunggyu; Yun, Hoonju; Song, Myongseop et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Tomaso Poggio; Seong-Whan Lee; Heinrich H. Bulthoff. Springer Verlag, 2000. p. 417-423 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1811).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - A fast circular edge detector for the iris region segmentation

AU - Park, Yeunggyu

AU - Yun, Hoonju

AU - Song, Myongseop

AU - Kim, Jaihie

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2000.

PY - 2000

Y1 - 2000

N2 - In this paper, we propose a fast circular edge detector for the iris region segmentation by detecting the inner and outer boundaries of the iris. In previous work, the circular edge detector which John G.Daugman proposed, searches the radius and the center of the iris to detect its outer boundary over an eye image. To do so, he used Gaussian filter to smooth texture patterns of the iris which cause its outer boundary to be detected incorrectly. Gaussian filtering requires much computation, especially when the filter size increases, so it takes much time to segment the iris region. In our algorithm, we could avoid procedure for Gaussian filtering by searching the radius and the center position of the iris from a position being independent of its texture patterns. In experimental results, the proposed algorithm is compared with the previous ones, the circular edge detector with Gaussian filter and the Sobel edge detector for the eye images having different pupil and iris center positions.

AB - In this paper, we propose a fast circular edge detector for the iris region segmentation by detecting the inner and outer boundaries of the iris. In previous work, the circular edge detector which John G.Daugman proposed, searches the radius and the center of the iris to detect its outer boundary over an eye image. To do so, he used Gaussian filter to smooth texture patterns of the iris which cause its outer boundary to be detected incorrectly. Gaussian filtering requires much computation, especially when the filter size increases, so it takes much time to segment the iris region. In our algorithm, we could avoid procedure for Gaussian filtering by searching the radius and the center position of the iris from a position being independent of its texture patterns. In experimental results, the proposed algorithm is compared with the previous ones, the circular edge detector with Gaussian filter and the Sobel edge detector for the eye images having different pupil and iris center positions.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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PB - Springer Verlag

T2 - 1st IEEE International Workshop on Biologically Motivated Computer Vision, BMCV 2000

Y2 - 15 May 2000 through 17 May 2000

ER -

Park Y, Yun H, Song M, Kim J. A fast circular edge detector for the iris region segmentation. In Poggio T, Lee S-W, Bulthoff HH, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2000. p. 417-423. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-45482-9_42

A fast circular edge detector for the iris region segmentation

Abstract

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