CFP last date
22 April 2024
Reseach Article

Improving the accuracy of Iris Recognition System using Neural Network and Particle swarm Optimization

by Nuzhat Faiz Shaikh, D. D. Doye
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 79 - Number 3
Year of Publication: 2013
Authors: Nuzhat Faiz Shaikh, D. D. Doye
10.5120/13718-1497

Nuzhat Faiz Shaikh, D. D. Doye . Improving the accuracy of Iris Recognition System using Neural Network and Particle swarm Optimization. International Journal of Computer Applications. 79, 3 ( October 2013), 1-6. DOI=10.5120/13718-1497

@article{ 10.5120/13718-1497,
author = { Nuzhat Faiz Shaikh, D. D. Doye },
title = { Improving the accuracy of Iris Recognition System using Neural Network and Particle swarm Optimization },
journal = { International Journal of Computer Applications },
issue_date = { October 2013 },
volume = { 79 },
number = { 3 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume79/number3/13718-1497/ },
doi = { 10.5120/13718-1497 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:52:01.887458+05:30
%A Nuzhat Faiz Shaikh
%A D. D. Doye
%T Improving the accuracy of Iris Recognition System using Neural Network and Particle swarm Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 79
%N 3
%P 1-6
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Iris recognition is the process of recognizing a person by analyzing the apparent pattern of his or her iris. Many techniques have been developed for iris recognition so far. Here, we propose a new iris recognition system with the help of local histogram and then optimized with FFBNN-PSO. In the proposed system, first the input eye images are preprocessed using adaptive median filter to remove the salt and pepper noise. Then, the features, which are extracted from the preprocessed image are given to FFBNN for training. In order to get accurate results, the FFBNN parameters are optimized using PSO.

References
  1. Li Ma, Tieniu Tan, Yunhong Wang and Dexin Zhang, "Efficient Iris Recognition by Characterizing Key Local Variations", IEEE Transactions on Image Processing, Vol. 13, No. 6, pp. 739-750 , June 2004.
  2. Jiali Cui, Yunhong Wang, Tieniu Tan, Li Ma and Zhenan Sun, "A fast and robust iris localization method based on texture segmentation", In proceedings of conference on Biometric Technology for Human Identification, China, Vol. 5401, pp. 1-8, 2004.
  3. Leila FallahAraghi, HamedShahhosseini and FarbodSetoudeh, "IRIS Recognition Using Neural Network", In proceedings of the International Multi Conference of Engineers and Computer Scientists, Vol. 1, Hong Kong, pp. 1-3, 2010.
  4. Dolly Choudhary, ShamikTiwari, Ajay Kumar Singh, "A Survey: Feature Extraction Methods for Iris Recognition", International Journal of Electronics Communication and Computer Technology, Vol. 2, No. 6, pp. 275-279, 2012.
  5. Hind Rostom Mohammed, "Gout Images Detection and Recognition by Neural Network", Journal of university of anbar for pure science, Vol. 5, No. 2, pp. 1-5, 2011.
  6. RahibH. Abiyev and KorayAltunkaya, "Personal Iris Recognition Using Neural Network", International Journal of Security and its Applications, Vol. 2, No. 2, pp. 41-50, 2008.
  7. N. F. Shaikh, Dr. D. D. Doye, "Combining the Goodness of Euler Number and Cumulative Sum to Achieve Higher Accuracy for Iris Recognition Systems", IJETCAS, Issue 5 Volume 2, pp. 183-188, June-August, 2013
  8. Chowhan and Shinde, "Iris Recognition Using Fuzzy Min-Max Neural Network", International Journal of Computer and Electrical Engineering, Vol. 3, No. 5, pp. 743-749, 2011.
  9. Gasser Auda and Mohamed Kamel, "CMNN: Cooperative Modular Neural Networks for pattern recognition", Pattern Recognition Letters, Vol. 18, No. 11, pp. 1391-1398, 1997.
  10. Omaima N. Ahmad AL-Allaf, AbdelfatahArefTamimi and Shahlla A. AbdAlKader "Artificial Neural Networks for Iris Recognition System: Comparisons between Different Models, Architectures and Algorithms", International Journal of Information and Communication Technology Research, Vol. 2, No. 10, pp. 744-752, 2012.
  11. Sun-Yuan Kung and Jenq-Neng Hwang, "Neural networks for intelligent multimedia processing", In proceedings of the IEEE, Vol. 86, No. 6, pp. 1244-1272, 1998.
  12. Ahmad M. Sarhan, "Iris Recognition Using Discrete Cosine Transform and Artificial Neural Networks", Journal of Computer Science, Vol. 5, No. 5, pp. 369-373, 2009.
  13. NareshBabu and Vaidehi, "Fuzzy Based IRIS Recognition System (FIRS) For Person Identification", In Proceedings of the IEEE-International Conference on Recent Trends in Information Technology, Chennai, Tamil Nadu, pp. 1005-1011, 2011
  14. http://iris. di. ubi. pt/ UBIRIS iris database
Index Terms

Computer Science
Information Sciences

Keywords

Feed Forward Back propagation Neural Network (FFBNN) Adaptive Median Filter Feature Extraction Iris Recognition Particle Swarm Optimization (PSO).