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

Performance Comparisons of Novel Feature Vector Selection Methods for Iris Recognition

by H.b. Kekre, Tanuja K. Sarode, Sunayana V Jadhav
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 49 - Number 16
Year of Publication: 2012
Authors: H.b. Kekre, Tanuja K. Sarode, Sunayana V Jadhav
10.5120/7712-1109

H.b. Kekre, Tanuja K. Sarode, Sunayana V Jadhav . Performance Comparisons of Novel Feature Vector Selection Methods for Iris Recognition. International Journal of Computer Applications. 49, 16 ( July 2012), 27-31. DOI=10.5120/7712-1109

@article{ 10.5120/7712-1109,
author = { H.b. Kekre, Tanuja K. Sarode, Sunayana V Jadhav },
title = { Performance Comparisons of Novel Feature Vector Selection Methods for Iris Recognition },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 49 },
number = { 16 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 27-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume49/number16/7712-1109/ },
doi = { 10.5120/7712-1109 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:46:49.972984+05:30
%A H.b. Kekre
%A Tanuja K. Sarode
%A Sunayana V Jadhav
%T Performance Comparisons of Novel Feature Vector Selection Methods for Iris Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 49
%N 16
%P 27-31
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The use of biometric systems has been increasingly encouraged by both government and private entities in order to replace or improve traditional security systems. The iris is commonly recognized as one of the most reliable biometric measures: it has a random morphogenesis and no genetic penetrance. In today's world, where terrorist attacks are on the rise, employment of infallible security systems is a must. This makes Iris recognition systems unavoidable in emerging security & authentication. In this paper an iris recognition system based on various transformation methods is proposed. A novel approach of selecting feature vector for performance comparison is implemented. Also the performance comparisons of all the transformation methods is done to achieve better accuracy and efficiency on the basis of number of correct sample identified. The proposed system does not need any pre-processing and segmentation. DCT, HAAR, and WALSH, SLANT and KEKRE'S Transforms are tested on different size of feature vector to get best possible results.

References
  1. H. B. Kekre, Tanuja K. Sarode, Vinayak Ashok Bharadi, Abhishek A. Agrawal, Rohan J. Arora, and Mahesh C. Nair "Performance Comparison of DCT and VQ Based Techniques for Iris Recognition" journal of electronic science and technology, vol. 8, no. 3, September 2010.
  2. Patnala S. R. Chandra Murty1, E. Sreenivasa, Reddy, and I. Ramesh Babu "Iris Recognition System Using Fractal Dimensions of Haar Patterns" International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 2, No. 3, September 2009.
  3. Atul Bansal, Ravinder Agarwal, R. K. Sharma "Trends in Iris Recognition Algorithms"2010 Fourth Asia International Conference on Mathematical/Analytical Modeling and Computer Simulation.
  4. H. B. Kekre, Tanuja K. Sarode, Vinayak Ashok Bharadi, Abhishek A. Agrawal, Rohan J. Arora, and Mahesh C. Nair "Iris Recognition Using Vector Quantization" journal of electronic science and technology, vol. 8, no. 3, September 2010.
  5. Http://phoenix. inf. upoml. cz/iris/download/, (referred on 18-08-2011, 10:00 p. m. ).
  6. Http://www. cl. cam. ac. uk
  7. Sarah E. Baker, Amanda Hentz, Kevin W. Bowyer, and Patrick J. Flynn "Degradation of Iris Recognition Performance Due to Non-Cosmetic Prescription Contact Lenses", june8, 2010.
  8. Z. Wei, X. Qui, Z. Sun, and T. Tan, "Counterfeit Iris Detection Based on Texture Analysis", Proc. of IEEE Int'l Conf. on Pattern Recognition vol. 5, no. 2, 2008.
  9. J. Dauman "How iris recognition works", IEEE Trans. CSVT, vol. 14, no. 1, pp. 21-30, 2004.
  10. Mahdi Jampour , Ali Naserasadi, Majid Estilayee, and Maryam Ashourzadeh, "Extract and Classification of Iris Images by Fractal Dimension and Efficient Color of Iris", International Journal of Computer Applications (0975 – 8887) ,Volume 18– No. 1, March 2011.
  11. Aditya Abhyankar, Stephanie Schuckers, "Novel Biorthogonal Wavelet based Iris Recognition for Robust Biometric System", International Journal of Computer Theory and Engineering 1793-8201, Vol. 2, No. 2 April, 2010.
  12. Christian Rathgeb, Andreas Uhl, "Bit Reliability driven Template Matching in Iris Recognition", 978-0-7695-4285, 2010 IEEE .
  13. W. Boles and B. Boashash, "A human identification technique using images of the iris and wavelet transform," IEEE Trans. Signal Processing, vol. 46, no. 4, pp. 1185-1188, 1998
  14. R. Wildes, "Iris recognition: an emerging biometric technology," Proc. of IEEE, vol. 85, no. 9, pp. 1348-1363, 1997.
  15. J. Cui, Y. Wang, T. Tan, L. Ma, and Z. Sun, "A fast and robust iris localization method based on texture segmentation," in Proc. of SPIE Defense and Security Symposium, Orlando, FL, Florida, 2004, pp. 401-408.
  16. R. P. Wildes, "Iris Recognition: An Emerging biometric technology" Proceeding of the IEEE, vol. 85, pp. 1348-1363, Sep 1997.
  17. D. Monro; S. Rakshit; D. Zhang, "DCT-Based Iris Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 29, pp. 586-595, 2007.
  18. N. Ritter. "Location of the pupil-iris border in slit-lamp images of the cornea" Proc. of the International Conference on Image Analysis and Processing, 1999.
Index Terms

Computer Science
Information Sciences

Keywords

Biometric Recognition Feature Vector partial feature vector Upper Diagonal feature vector

Powered by PhDFocusTM