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Iris Recognition using Left and Right Iris Feature of the Human Eye for Biometric Security System

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International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 50 - Number 12
Year of Publication: 2012
Authors:
B. Thiyaneswaran
S. Padma
10.5120/7826-1123

B Thiyaneswaran and S Padma. Article: Iris Recognition using Left and Right Iris Feature of the Human Eye for Biometric Security System. International Journal of Computer Applications 50(12):37-41, July 2012. Full text available. BibTeX

@article{key:article,
	author = {B. Thiyaneswaran and S. Padma},
	title = {Article: Iris Recognition using Left and Right Iris Feature of the Human Eye for Biometric Security System},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {50},
	number = {12},
	pages = {37-41},
	month = {July},
	note = {Full text available}
}

Abstract

Iris recognition plays an important role in the Biometric authentication. The eye lids, lashes and flash light impressions are hazard, which in turn reduces successive iris recognition rate. The proposed method includes the preprocessing of images such as image filter, morphological operations, and edge detection, which finds the exact pupil part. The proposed method uses the MLRP algorithm, to identify the exact iris layers rather than the existing methods. The key feature is extracted in iris layer. The method is applied on both left and right iris, which gives unique key between left and right eye for every person. The extracted key feature identifies the eye even in the different eye position, which gives the repeatability. The proposed method is tested with the CASIA data base iris images, which consists of left and right eye set for the different human. The proposed method reduces the FAR to 15. 6% and FRR to 14%.

References

  • Pierre E. Abi-Char, Bachar El-Hassan, Abdallah Mhamed. 2011. An Enhanced Authenticated Key Agreement Protocol with a Neural Network-based Model for Joining-Phase in Mobile Environments, International Journal of Engineering and Industries, Vol. 2, No. 2.
  • Thiyaneswaran B, Kandiban R, Jayakumar K. S. 2012. Localization of iris region using MLRP algorithm intended for biometric applications, European journal of scientific research(EJSR), Vol. 74, Issue 1, pp. 126-133
  • Debnath Bhattacharyya, Rahul Ranjan, Farkhod Alisherov A. and Minkyu Choi. 2009. Biometric Authentication, A Review, International Journal of u- and e- Service, Science and Technology Vol. 2, No. 3.
  • Chowhan. S. S. , Shinde. G. N, COCSIT, Latur. 2008. Iris biometric recognition application in security management, Image and Signal Processing, CISP '08, pp. 661 – 665.
  • L. Masek. 2003. Recognition of Human Iris Patterns for Biometric Identification M. Thesis, The University of Western Australia. Vol. 3.
  • Bhawna Chohan, Shailija Shukla. 2010. Analysis of statistical feature extraction for iris recognition system using Laplacian if Gaussian filter, International journal of applied engineering research, Vol. 1.
  • Anguraj K, Kandiban R, Jayakumar K S. 2012. Facial paralysis diseases level detection using CEM algorithm for clinical applications, European journal of scientific research, Vol77, Issue. 4, PP 543-548.
  • Jong Gook Ko, Youn Gil, Kvo Chung. 2007. A Noval and efficient feature extraction method for iris recognition, ETRI Journal, Vol. 29, PP 399-401.
  • Birgale, L. V. , Kokare, M. 2009. Iris recognition using discrete wavelet transform, Digital Image Processing, International Conference, pp. 147 – 151.
  • Zhongliang Luo, Tusheng Lin. , 2008. Detection of Non-iris Region in the Iris Recognition, Computer Science and Computational Technology, ISCSCT '08, pp. 45 – 48.
  • Wibowo, E. P. , Maulana, W. S. 2009. Real-Time Iris Recognition System Using a Proposed Method, International Conference on Signal Processing Systems, pp. 98 – 102.
  • J. Sauvola, M. Pietikäinen. 2000. Adaptive document image binarization. Pattern Recognition 33, pp. 225-236.
  • Asano, M. , Takano, H. , Nakamura, K. 2010. Iris detection method using particle filter and edge directional features, World Automation Congress (WAC), pp. 1 – 6.
  • Horapong. K. , Sreecholpech. J. , Thainimit. S. , Areekul. V. 2005. An Iris Verification Using Edge Detection, Information, Communications and Signal Processing, pp. 1434 – 1438.
  • Topi Maenpaa. 2005. An Iterative Algorithm for Fast Iris Detection. IWBRS. Beijing, China. pp. 127-134.
  • Srinivasa kumar, G. Ramaswamy, D. Ravikiran, P. sirisha rani. 2009. A novel approach for an accurate human Identification through iris recognition using bit plane slicing and normalization, Journal of theoretical and applied information technology,pp. 531-537.
  • Arti Dhiman, Ashok Kumar, Manoj Arora. 2011. Design of a Real Time System for Acquiring and Analyzing Iris Images in MATLAB, IJECT, Vol. 2, Issue 3, PP. 106-110.
  • Christopher Boyce, Arun Ross, Matthew Monaco, Lawrence Hornak and Xin Li. 2006. Multispectral Iris Analysis: A Preliminary Study, International Workshop On Computer Biometrics, pp 211-216.
  • Gite h. r. , Mahender C. N. 2011. Iris Code Generation and Recognition, International Journal of Machine Intelligence, Volume 3, Issue 3, pp-103-107.