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Iris Recognition using Gray Level Co-occurrence Matrix and Hausdorff Dimension

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Amol M. Patil, Dilip S. Patil, Pravin S. Patil
10.5120/ijca2016907953

Amol M Patil, Dilip S Patil and Pravin S Patil. Article: Iris Recognition using Gray Level Co-occurrence Matrix and Hausdorff Dimension. International Journal of Computer Applications 133(8):29-34, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Amol M. Patil and Dilip S. Patil and Pravin S. Patil},
	title = {Article: Iris Recognition using Gray Level Co-occurrence Matrix and Hausdorff Dimension},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {133},
	number = {8},
	pages = {29-34},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

Biometrics authentication is the only accurate solution for personal identification and security problems. Password incorrect use and misapplication, intentional and inadvertent is a gaping hole in security. These results are mainly occurs due to Poor human judgment, carelessness and due to tactlessness. Biometric removes all these types of security mistakes. In iris recognition system identification and verification is one of the efficient method. The objective of this proposed system is to analyze the performance of iris. The segmentation of the iris utilizes shape, intensity, and location information for pupil or iris localization and performs normalization of the iris region by unwrapping the circular region into a rectangular region. The feature extraction of iris was done by biometrics GLCM (Gray Scale Co-occurrence Matrix) and HD (Hausdorff Dimension. The BGM (Biometric Graph Matching) algorithm is used, which is used to match the graph between the training image and test image of the iris biometric. The BGM algorithm uses graph topology to define different feature values of the iris templates. A SVM (Support Vector Machine) classifier is used to distinguish between genuine and imposter. The results give better performance and authentication than the existing method.

References

  1. Pravin S.Patil “Research on Iris Region Localization Algorithms” Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 4, Issue 10 ( Part - 3), October 2014, pp.111-119
  2. Yulin Si, Jiangyuan Mei, and HuijunGao, “Novel Approaches to Improve Robustness, Accuracy and Rapidity of Iris Recognition Systems” IEEE transactions on industrial informatics, vol. 8, no. 1, pp.110-117, February 2012.
  3. Seyed Mehdi Lajevardi, Arathi Arakala, Stephen A. Davis, and Kathy J. Horadam, “Retina Verification System Based on Biometric Graph Matching” IEEE Transactions on Image Processing, vol. 22, no. 9, pp.3625- 3635 September 2013.
  4. Pravin S.Patil “Iris Recognition Based On Gaussian-Hermite Moments” International Journal on Computer Science and Engineering (IJCSE) ISSN : 0975-3397, Vol. 4 No. 11 Nov 2012
  5. “Daughman’s Algorithm method For Iris Recognition-A Biometric Approach” International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 6, June 2012)
  6. Hugo Proenca, “Toward Covert Iris Biometric Recognition: Experimental Results From the NICE Contests” IEEE Transactions On Information Forensics And Security, vol.7, no. 2, pp.798-808, April 2012.
  7. Somnath Dey, “Iris Data Indexing Method Using Gabor Energy Features” IEEE Transactions on Information Forensics and Security, vol.7, no. 4, pp.1192-1203, August 2012.

Keywords

SVM (Support Vector Machine), BGM (Biometric Graph Matching), Segmentation, IRIS