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

Collarette Region Recognition based on Wavelets and Direct Linear Discriminant Analysis

by Akanksha Joshi, Abhishek Gangwar, Zia Saquib
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 40 - Number 9
Year of Publication: 2012
Authors: Akanksha Joshi, Abhishek Gangwar, Zia Saquib
10.5120/4996-7270

Akanksha Joshi, Abhishek Gangwar, Zia Saquib . Collarette Region Recognition based on Wavelets and Direct Linear Discriminant Analysis. International Journal of Computer Applications. 40, 9 ( February 2012), 35-39. DOI=10.5120/4996-7270

@article{ 10.5120/4996-7270,
author = { Akanksha Joshi, Abhishek Gangwar, Zia Saquib },
title = { Collarette Region Recognition based on Wavelets and Direct Linear Discriminant Analysis },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 40 },
number = { 9 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 35-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume40/number9/4996-7270/ },
doi = { 10.5120/4996-7270 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:27:39.771427+05:30
%A Akanksha Joshi
%A Abhishek Gangwar
%A Zia Saquib
%T Collarette Region Recognition based on Wavelets and Direct Linear Discriminant Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 40
%N 9
%P 35-39
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Iris recognition is seen as a highly reliable biometric technology. The performance of iris recognition is severely impacted when encountering poor quality images. The selection of the features subset and the classification is an important issue for iris biometrics. Here, we explored the contribution of collarette region in identifying a person. We applied five level haar wavelet decomposition for collarette region feature extraction and used the second level approximation coefficients combined with fifth level vertical coefficients for better accuracy. Then, we applied Direct Linear Discriminant Analysis (DLDA) to produce discriminative low-dimensional feature vectors. The approach is evaluated on CASIA Iris Interval database and we achieved 98.96% accuracy using the collarette region which is a significant improvement in the performance of recognition.

References
  1. J.G. Daugman. The importance of being random: Statistical principles of iris recognition. Pattern recognition, 36(2), (2003) 279–291
  2. J. G. Daugman, "How iris recognition works", IEEE Trans. on circuits and Systems for Video Technology, vol. 14, no. 1, January 2004, pp. 21-30
  3. W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld. Face recognition: A literature survey. ACM Computing Surveys, 35(4):399–458, 2003.
  4. John Daugman. Biometric personal identification system based on iris analysis. U.S. Patent No. 5,291,560, Mar 1994.
  5. T. Chuan Chen, K. Liang Chung: An Efficient Randomized Algorithm for Detecting Circles. Computer Vision and Image Understanding Vol. 83 (2001) 172-191
  6. J. Canny: A Computational Approach to Edge Detection. IEEE Transaction on Pattern Analysis and Machine Intelligence Vol. 8 (1986) 679-714
  7. A Direct LDA Algorithm for High-Dimensional Data with Application to Face Recognition Hua Yu 1, Jie Yang Interactive System Labs, Carnegie Mellon University, Pittsburgh, PA 15213.
  8. D. Swets and J. Weng. Using discriminant eigenfeatures for image retrieval. PAMI, 18(8):831-836, August 1996.
  9. L. Chen, H. Liao, M. Ko, J. Lin, and G. Yu. A new lda-based face recognition system which can solve the small sample size problem. Pattern Recognition, 33(10):1713{1726, Oct 2000.
  10. H. Sung, J. Lim, J. Park, and Y. Lee, “Iris recognition using collarette boundary localization”. Internat. Conf. on Pattern Recog. vol. 04, 2004.
  11. M. W. Frazier, “An Introduction to Wavelets through Linear algebra”, Springer, 1999.
  12. CASIA Iris Image Database. http://www.sinobiometrics.com
  13. Richard P. Wildes. Iris recognition: an emerging biometric technology. In Proceedings of the IEEE, vol. 85, no.9, pages 1348–1363, U.S.A., September 1997.
  14. Abhishek Gangwar, Akanksha Joshi, Renu Sharma, Zia Saquib, “Robust Iris Templates for Efficient Person Identification” Internat. Conf. on Signal, Image Processing and Pattern Recognition, Springer 2012.
  15. K. Roy and P. Bhattacharya "An Iris Recognition Method based on Zigzag Collarette Area and Asymmetrical Support Vector Machines", in IEEE conference on Systems, Man, and Cybernetics (SMC'2006), 8-11 October, 2006, Taiwan. Pages 861-865.
  16. W. Boles and B. Boashash, "Human Identification Technique Using Images of the Iris and Wavelet Transform", IEEE Trans. on Signal Processing, vol. 46, no. 4, pp. 1185-1188, 1998.
  17. Hanho Sung; Jaekyung Lim; Ji-hyun Park; Yillbyung Lee; , "Iris recognition using collarette boundary localization," Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on , vol.4, no., pp. 857- 860 Vol.4, 23-26 Aug. 2004 doi: 10.1109/ICPR.2004.1333907.
Index Terms

Computer Science
Information Sciences

Keywords

Collarette Feature Extraction Wavelet Transform.

Powered by PhDFocusTM