We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Integration of Morphological Segmentation and Canny Edge Detection for Iris Recognition

by K. Lashmi Priya, D. Christopher Durairaj
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 80 - Number 2
Year of Publication: 2013
Authors: K. Lashmi Priya, D. Christopher Durairaj
10.5120/13834-0811

K. Lashmi Priya, D. Christopher Durairaj . Integration of Morphological Segmentation and Canny Edge Detection for Iris Recognition. International Journal of Computer Applications. 80, 2 ( October 2013), 26-31. DOI=10.5120/13834-0811

@article{ 10.5120/13834-0811,
author = { K. Lashmi Priya, D. Christopher Durairaj },
title = { Integration of Morphological Segmentation and Canny Edge Detection for Iris Recognition },
journal = { International Journal of Computer Applications },
issue_date = { October 2013 },
volume = { 80 },
number = { 2 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 26-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume80/number2/13834-0811/ },
doi = { 10.5120/13834-0811 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:53:31.037348+05:30
%A K. Lashmi Priya
%A D. Christopher Durairaj
%T Integration of Morphological Segmentation and Canny Edge Detection for Iris Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 80
%N 2
%P 26-31
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A new approach to iris recognition system is proposed in this paper. The iris images are captured using digital camera. The edges of the eye image are traced using canny edge detection. Segmentation is done to find the inner and outer edge of the iris region. Segmentation is done by selecting appropriate threshold approximately in the range 60 to 80 and applying Extended Minima transform. Binary code representation via phase of Daubechies wavelet is computed from each iris image and a minimum Euclidean distance classifier is applied for matching process. This approach is proved to be efficient and has less error rate for iris images captured using digital camera.

References
  1. J. Daugman 2002 How Iris Recognition works, proceedings of 2002 International conference on Image processing, vol. 1, 2002.
  2. Wildes R. P. 1997 Iris Recognition: An Emerging Biometric Technology, Proceedings of the IEEE, 85(9), l348-1363.
  3. A. Poursaberi, B. N. Araabi 2004 A Fast Morphological Algorithm for Iris Detection in Eye images, 6th International Conference on Intelligent Systems, Kerman, Iran.
  4. J. Huang, Y. Wang, T. Tan, and J. Cui 2004 A New Iris Segmentation Method for Recognition, Proc. of 17th Int'Conf. on Pattern Recognition (ICPR'04), vol. 3.
  5. Boles W. , Boashah B. 1998 A Human Identification Technique Using Images of the Iris and Wavelet Transform, IEEE Trans on Signal Processing, Vol. 46, 1185-1188, 1998.
  6. Poursaberi, A. , and Araabi B. N. 2005 "A Novel Iris Recognition System using Morphological Edge Detector and Wavelet Phase Features". ICGST International Journal on Graphics, Vision and Image Processing, 5(6), 262-267.
  7. Saravanan, C. 2010 Color Image to Grayscale Image Conversion,IEEE Trans, on Computer Engineering and Applications (ICCEA), Second International Conference, Vol. 2,196 – 199.
  8. Jinyu Zuo 2008 A new approach for iris segmentation, IEEE Trans, Computer society conference on Computer Vision and Pattern Recognition Workshops,1-6.
  9. Bei Yan, A robust algorithm for pupil center detection, IEEE Conference on Industrial Electronics and Applications,413-417.
  10. J. Canny 1986 A Computational Approach to Edge Detection IEEE Trans of Pattern Analysis and Machine Intelligence, vol. PAMI-8, no. 6, pages 679-698.
  11. Joshi, N. P. , Lamba, R. K. , Shah, D. U. and Ghadia, B. V. 2011 Implementation of various approaches for iris image normalization, IEEE Trans, conference on Engineering, Nirma University International , 1-5.
  12. Conjeti, S. 2012 Patient identification using high-confidence wavelet based Iris Pattern recognition, on Biomedical and Health Informatics,628-631.
  13. Vonesch, C. 2011 Generalized Daubechies wavelets, IEEE Trans on Acoustics, Speech, and Signal Processing, vol. 4, 593-596.
  14. Moi, S. H. 2009 Iris Bioetric Cryptography for Identity Document, IEEE Trans, on Soft Computing and Pattern Recognition, 736-741.
  15. Peng-Fei Zhang 2004 A novel iris recognition method based on feature fusion, IEEE Trans, Machine Learning and Cybernetics, vol. 6,3661 – 3665, 2004.
Index Terms

Computer Science
Information Sciences

Keywords

Extended Minima transform Rubbersheet model Daubecheis wavelet decomposition

Powered by PhDFocusTM