CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Fragmented Iris Recognition System using BPNN

by A. Murugan, G. Savithiri
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 36 - Number 4
Year of Publication: 2011
Authors: A. Murugan, G. Savithiri
10.5120/4483-6309

A. Murugan, G. Savithiri . Fragmented Iris Recognition System using BPNN. International Journal of Computer Applications. 36, 4 ( December 2011), 28-33. DOI=10.5120/4483-6309

@article{ 10.5120/4483-6309,
author = { A. Murugan, G. Savithiri },
title = { Fragmented Iris Recognition System using BPNN },
journal = { International Journal of Computer Applications },
issue_date = { December 2011 },
volume = { 36 },
number = { 4 },
month = { December },
year = { 2011 },
issn = { 0975-8887 },
pages = { 28-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume36/number4/4483-6309/ },
doi = { 10.5120/4483-6309 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:22:18.063615+05:30
%A A. Murugan
%A G. Savithiri
%T Fragmented Iris Recognition System using BPNN
%J International Journal of Computer Applications
%@ 0975-8887
%V 36
%N 4
%P 28-33
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The authentication of people using Iris based recognition system is the most reliable biometric traits due to its stability, invariant and distinctive features for personal identification. Iris recognition consists of localization of the Iris region, extracting Iris features, generation of data set of Iris images and then Iris pattern recognition. This paper presents Iris recognition system based on partial portion of Iris patterns using Back Propagation Neural Network (BPNN). Experimental results have demonstrated the effectiveness of the propose system in terms of recognition accuracy in comparison with the previous methods.

References
  1. Daugman J, “Complete Discrete 2-D Gabor Transforms by Neural Networks for Image Analysis and Compression”, IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 36, no. 7, July 1988, pp. 1169-1179.
  2. Daugman. J, “High Confidence visual Recognition of Persons by a Test of Statistical Independence”, IEEE transactions on Pattern Analysis and Machine Intelligence, Vol. 15, No. 11, pp. 1148-1161, 1993.
  3. N. Dala and B.Tiggs, “Histograms of oriented gradients for human detection”, IEEE CS Conference on Computer Vision and Pattern Recognition, pp.886-893, 2005.
  4. T.Ojala, M.Pietikainen and D.Harwood. ”A comparative study of texture measures with classification based on feature distributions”. Pattern Recognition, January 1996.
  5. Maheswari, P.Anbalagan and T.Priya, “Efficient Iris Recognition through Improvement in Iris Segmentation Algorithm”, ICGST-GVIP Journal, ISSN: 1687-398X, Vol 8, Issue 2, pp. 29-35, 2008.
  6. Leila Fallah Araghi, Hamed Shahhossseini and Farbod Setoudeh, “IRIS Recognition Using Neural Network”, Proceedings of the International Multi Conference of Engineers and Computer Scientists 2010 (IMECS), Vol I, March 2010.
  7. Sameul Adebayo Daramoal and Daniel ,”Automatic Ear Recognition System using Back Propagation Neural Network”, International Journal of Video & Image Processing and Network Security (IJVIPNS-IJENS), Vol. 11 No.01.pp 28-32, Feb 2011.
  8. Shylaja S S & et.al. ”Feed Forward Neural Network Based Eye Localization and Recognition Using Hough Transform”, International Journal of Advance Computer Science and Applications, Vol.2, No.3, pp.104-108, March 2011.
  9. Mohammod Abul Kashem & et. al, ”Face Recognition System Based on Principal Component Analysis with Back Propagation Neural Networks”, International Journal of Scientific & Engineering Research, Vol. 2, Issue 6, pp 1-10, June 2011.
  10. S.R. Ganorkar and J.A. Deshpande, “Person Identification Using Iris Recognition”, International Journal of Engineering and Technology, Vol.3, No. 1, pp 40-43, Feb 2011.
  11. G. Savithiri and A. Murugan, ”Performance Analysis of Iris Recognition Algorithms”, Proceedings of the International Conference on Millennium Development Goals (MDGICT) pp. 294-299, Dec. 2009.
  12. G. Savithiri and A. Murugan, “Iris Recognition Technique using Gaussian Pyramid”, Proceedings of the International Conference on Recent Trends in Business Administration and Information Processing, (BAIP), CCIS 70, pp-325-331, Springer-Verleg Berlin Heidelberg , March 2010.
  13. G. Savithiri and A. Murugan, “Iris Recognition Technique using Gaussian Pyramid Compression and Modified Distance Measures”, Journal of Computational Intelligence in Bioinformatics, Vol 3, No. 1, pp 101-110, July 2010.
  14. A. Murugan and G. Savithiri, “Feature Extraction on Half Iris for Personal Identification”, Proc. IEEE International Conference on Signal and Image Processing (ICSIP), pp 197-200, Dec 2010.
  15. G. Savithiri and A. Murugan, ”Performance Analysis on Half Iris Feature Extraction using GW, LBP and HOG”, International Journal of Computer Applications (IJCA), Vol.22, No.2, pp 27-32, May 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Biometric Iris recognition Back Propagation Neural Network Web Access Pattern Relative Dotted Sequence Path (WRDSP) Iris patterns

Powered by PhDFocusTM