CFP last date
20 January 2025
Reseach Article

Enhanced Human Iris Recognition System based on Procedure of Authentication System

by Rajvir Kaur, Ishpreet Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 9
Year of Publication: 2016
Authors: Rajvir Kaur, Ishpreet Singh
10.5120/ijca2016910903

Rajvir Kaur, Ishpreet Singh . Enhanced Human Iris Recognition System based on Procedure of Authentication System. International Journal of Computer Applications. 146, 9 ( Jul 2016), 36-40. DOI=10.5120/ijca2016910903

@article{ 10.5120/ijca2016910903,
author = { Rajvir Kaur, Ishpreet Singh },
title = { Enhanced Human Iris Recognition System based on Procedure of Authentication System },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 9 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 36-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume146/number9/25429-2016910903/ },
doi = { 10.5120/ijca2016910903 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:50:00.242648+05:30
%A Rajvir Kaur
%A Ishpreet Singh
%T Enhanced Human Iris Recognition System based on Procedure of Authentication System
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 9
%P 36-40
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Biometrics refers to the recognition or confirmation of an individual based on certain unique features or characteristics. Biometric identifiers are the characteristic and quantifiablefeatures that are used to label and describe individuals. Iris recognition and favour because of its high recognition rate, non-invasive and simple algorithm and other advantages, in a selection of biometric classification technology is very prominent. The iris texture feature extraction is the core of the iris acknowledgment algorithm. Fractal geometry theory provide new ideas and methods to express nonlinear image information, the fractal dimension is an imperativelimitation of fractal geometry, is a measure of complexity of irregular modify, covering envelop dimension can better replicate the graphics changes in different resolution characteristics; absent is the fractal dimension and autonomous statistics, is a supplement to the fractal dimension, overcome the different texture description may have the same fractal measurement of the problem. The biometric template is usually created using some sort of arithmetical operations. If a personality wants to be identified by the system, then first a digitized image of their eye is first shaped, and then a biometric pattern is created for their iris region. This biometric pattern is compared with all the other pre-existing templates in the database using certain matching algorithms in order to get the identification of the individual.In this paper, we describe the novel techniques that are developed to create an Irisappreciation System, A current survey of iris biometric research from its inception till now lists approximately 29publications. Research in iris biometrics has expanded so much that, although covering onlythese years and intentionally being discriminating about treatment, this new survey lists a larger number of references.

References
  1. Lye Wi Liam, Ali Chekima, Liau Chung Fan and Jamal Ahmad Dargham, “Iris Recognition using Self-Organizing Neural Network”, IEEE 2002 Student Conference on Research and Development Proceedings, Shah Alam, Malaysia, pp. 169-172.
  2. Eric Sung, Xilin Chen, Jie Zhu and Jie Yang, “Towards non-cooperative iris recognition systems”, Seventh international Conference on Control, Automation, Robotics And Vision (ICARCV’02), Dec. 2002, Singapore, pp. 990-995.
  3. Jiali Cui, Yunhong Wang, JunZhou Huang, Tieniu Tan and Zhenan Sun, “An Iris Image Synthesis Method Based on PCA and Super-resolution”, IEEE CS Proceedings of the 17th International Conference on Pattern Recognition (ICPR’04), 23-26 August 2004, Cambridge, UK, Vol. 4, pp. 471-474.
  4. HyungGu Lee, Seungin Noh, KwanghyukBae, Kang-Ryoung Park and Jaihie Kim, “Invariant biometric code extraction”, IEEE Intelligent Signal Processing and Communication Systems (ISPACS 2004), 18-19 November, 2004, Seoul, Korea, pp. 181-184.
  5. Xianzhao et.al, “Improved Empirical Mode Decomposition Algorithm of Processing Complex Signal for IoT Application”, International Journal of Distributed Sensor Networks, Vol. 2015, 2015.
  6. Raedet.a;, “A Powerful yet Efficient Iris Recognition Based on Local Binary Quantization”, Information Technology And Control, Vol. 43, 2014.
  7. Christopher, “A Fast Empirical Mode Decomposition Technique for Nonstationary Nonlinear Time Series”, Elsevier, 2005.
  8. Wei Kin et.al, “Iris recognition based on bidimensional empirical mode decompositionand fractal dimension”, Information Sciences, vol. 221, pp. 439–451, 2013.
  9. Azade et.al, “Iris Recognition based on Wavelet Transform and Probabilistic Neural Network”, International Journal of Computer Trends and Technology (IJCTT, Vol. 14, 2014.
  10. Tomasq et.al, “Selection of parameters in iris recognition system“, Multimedia tools and Applications, Vol. 68, Issue 1, pp 193-208, 2014.
  11. Haiqing et.al, “A Brief Survey on Recent Progress in Iris Recognition”, Biometric Recognition, Vol. 8833, pp 288-300, 2014.
  12. Kiran B. Raja, R. Raghavendra, Vinay Krishna Vemuri, and Christoph Busch. 2015. Smartphone based visible iris recognition using deep sparse filtering. Pattern Recogn. Lett. 57, C (May 2015), 33-42.
  13. Jain Zhen, “Iris Recognition based on Block Theory and Self-adaptive Feature Selection”, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 8, No. 2, pp. 115-126, 2015.
  14. Yongqiang LI, “Iris Recognition Algorithm based on MMC-SPP”, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 8, No. 2, pp. 1-10, 2015.
  15. Bhuiyan, S.M.A.; Khan, J.F.; Adhami, R.R., "A bidimensional empirical mode decomposition method for color image processing," in Signal Processing Systems (SIPS), 2010 IEEE Workshop on , vol., no., pp.272-277, 6-8 Oct. 2010.
  16. PP. Sinha et.al, “A Review on Bidimensional Empirical Mode Decomposition”, International Journal of Science and Research (IJSR), Vol. 14, 2013.
  17. Donghoh Kim; Minjeong Park; Hee-Seok Oh, "Bidimensional Statistical Empirical Mode Decomposition," in Signal Processing Letters, IEEE , vol.19, no.4, pp.191-194, April 2012.
  18. Weiki Yuan, Zhonghua Lin and Lu Xu, “A Rapid Iris Location Method Based on the Structure of Human Eyes”, 27th Annual Conference of the IEEE Engineering in Medicine and Biology Society, 1-4 September, 2005, Shanghai, China, pp. 3020-3023.
Index Terms

Computer Science
Information Sciences

Keywords

Iris Recognition Biometric authentication fractal geometry theory and feature extraction process.