CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Iris Recognition based on Wavelets

by S V Sheela, P A Vijaya
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 26 - Number 11
Year of Publication: 2011
Authors: S V Sheela, P A Vijaya
10.5120/3166-4381

S V Sheela, P A Vijaya . Iris Recognition based on Wavelets. International Journal of Computer Applications. 26, 11 ( July 2011), 47-54. DOI=10.5120/3166-4381

@article{ 10.5120/3166-4381,
author = { S V Sheela, P A Vijaya },
title = { Iris Recognition based on Wavelets },
journal = { International Journal of Computer Applications },
issue_date = { July 2011 },
volume = { 26 },
number = { 11 },
month = { July },
year = { 2011 },
issn = { 0975-8887 },
pages = { 47-54 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume26/number11/3166-4381/ },
doi = { 10.5120/3166-4381 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:12:34.573610+05:30
%A S V Sheela
%A P A Vijaya
%T Iris Recognition based on Wavelets
%J International Journal of Computer Applications
%@ 0975-8887
%V 26
%N 11
%P 47-54
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recognition refers to the problem of establishing a subject’s identity from a set of already known identities. Iris recognition system identifies a person from the database of iris images. Iris patterns form distinguishing characteristics for an individual. The potency of iris recognition lies in its textual information. Iris based security systems capture iris patterns of individuals and match the patterns against the record in available databases. In this paper, wavelet decomposition is applied on iris patterns. The magnitude of coefficients aid in the generation of unique code for recognition. The recognition rate of 100% is achieved.

References
  1. Rafeal C. Gonzalez and Richard E. Woods. 2005. Digital Image Processing, 2nd Edition, Pearson Education.
  2. C.A.Bouman. 2011. Connected Component Analysis, Digital Image Processing, https://engineering.purdue.edu/bouman/ece637/notes/pdf/ConnectComp.pdf.
  3. Abeer George Ghuneim. 2000. Defining Connectivity, http://www.imageprocessingplace.com.
  4. Earl Gose, Richard Johnsonbaugh and Steve Jost. 2006. Pattern Recognition and Image Analysis, 1st Edition, Prentice-Hall of India Private Limited.
  5. Robi Polikar. 1999. The Wavelet Tutorial.
  6. Amara Graps. 1995. An Introduction to Wavelets, Computing in Science and Engineering, vol. 2, no. 2, pp. 50-61.
  7. Mohammed A. Salem, Nivin Ghamry and Beate Meffert. 2009. Daubechies Versus Biorthogonal Wavelets for Moving Object Detection in Traffic Monitoring Systems, http://www2.informatik.hu-berlin.de/sam/preprint/m.
  8. Junxing Ma, Jijun Xue, Shengjun Yang and Zhengjia He. 2003. A study of the construction and application of a Daubechies wavelet-based beam element, Finite Elements in Analysis and Design, vol. 39, pp. 965-975.
  9. J. Daugman. 1993. High Confidence Visual Recognition by a Test of Statistical Independence, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, No.11, pp.1148-1161.
  10. Karen Hollingsworth, Sarah Baker, Sarah Ring, Kevin W. Bowyer and Patrick J.Flynn. 2009. Recent Research Results In Iris Biometrics, SPIE 7306B: Biometric Technology for Human Identification VI.
  11. R. Wildes, J. Asmuth, G. Green, S. Hsu, R. Kolczynski, J. Matey, and S. McBride. 1996. A machine-vision system for iris recognition, Mach. Vis. Applic., vol. 9, pp. 1-8.
  12. Li Ma, Tieniu Tan, Yunhong Wang, Dexin Zhang. 2003. Personal Identification based on Iris Texture Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.25, No.12, pp. 1519-1533.
  13. Zhenan Sun, Yunhong Wang, Tieniu Tan and Jiali Cui, 2004. Improving Iris Recognition Accuracy Via Cascaded Classifiers, Biometric Authentication. LNCS 3072, pp. 1-11.
  14. H. Proena and L.A. Alexandre. 2006. Iris Segmentation Methodology for Noncooperative Iris Recognition. IEE Proc. Vision, Image, and Signal Processing, vol. 153, no. 2, pp. 199-205.
  15. Aditya Abhyankar and Stephanie Schuckers. 2010. Novel Biorthogonal Wavelet based Iris Recognition for Robust Biometric System, International Journal of Computer Theory and Engineering, vol. 2, No. 2, pp. 1793-8201.
  16. Agus Harjoko, Sri Hartati and Henry Dwiyasa. 2009. A Method for Iris Recognition Based on 1D Coiflet Wavelet.World Academy of Science, Engineering and Technology, vol. 56, pp. 1-4.
  17. Zhengmao Ye, Yongmao Ye, Hang Yin, Habib Mohamadian. 2009. Integration of Wavelet Fusion and Adaptive Contrast Stretching for Object Recognition with Quantitative Information Assessment, ICGST-GVIP Journal, vol. 8, Issue V, pp. 33-42.
  18. Xiaomei Liu, Bowyer K.W. and Flynn P.J. 2005. Experimental Evaluation of Iris Recognition, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 158-165.
Index Terms

Computer Science
Information Sciences

Keywords

Wavelet decomposition unique code magnitude of detailed coefficients core and non-core segments

Powered by PhDFocusTM