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Iris Recognition System based on Multi-resolution Analysis and Support Vector Machine

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 173 - Number 7
Year of Publication: 2017
Authors:
Manisha Nirgude, Sachine Gengaje
10.5120/ijca2017915366

Manisha Nirgude and Sachine Gengaje. Iris Recognition System based on Multi-resolution Analysis and Support Vector Machine. International Journal of Computer Applications 173(7):28-33, September 2017. BibTeX

@article{10.5120/ijca2017915366,
	author = {Manisha Nirgude and Sachine Gengaje},
	title = {Iris Recognition System based on Multi-resolution Analysis and Support Vector Machine},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2017},
	volume = {173},
	number = {7},
	month = {Sep},
	year = {2017},
	issn = {0975-8887},
	pages = {28-33},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume173/number7/28349-2017915366},
	doi = {10.5120/ijca2017915366},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Iris recognition system is becoming more popular day by day and is being used in many sectors for authentication replacing traditional methods like password, ATM etc. Iris recognition system is more accurate due to unique and stable iris patterns. Here, a feature extraction method based on multi-resolution analysis is proposed. Iris image is represented at multiple resolution levels and feature vector is formed by combining detailed information obtained at different resolution levels. Further, support vector machine classifier is used for recognition purpose to handle nonlinearity of features. Experiment is performed using CASIA 3.0 database with an objective to arrive at optimum number of features with high recognition rate.

References

  1. J. Daugman (2004). “How iris recognition works”, IEEE Trans. CSVT, vol. 14, no.1, pp. 21 – 30
  2. Ahmad M. Sarhan, “Iris Recognition Using Discrete Cosine Transform and Artificial Neural Networks”, Journal of Computer Science 5 (5): 369-373, 2009
  3. Doggart, J.H., Ocular Signs in Slit-Lamp Microscopy, page 27, Kimpton (1949)
  4. Adler, F.H., Physiology of the Eye, Chapter VI, page 143, Mosby (1953)
  5. R. Wildes, J. Asmuth, G. Green, S. Hsu, R. Kolczynski, J. Matey, S. McBride, “A system for automated iris recognition”, Proceedings IEEE Workshop on Applications of Computer Vision, Sarasota, FL, pp. 121-128, 1994
  6. C.C. Teo and H.T. Ewe, “An Efficient One-Dimensional Fractal Analysis for Iris Recognition”, Proceedings of the 13th WSCG International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2005, pp. 157-160.
  7. K. Grabowski, W. Sankowski, M. Zubert, and M. Napieralska, “Reliable Iris Localization Method with Application to Iris Recognition in Near Infrared Light”, MIXDES 2006.
  8. Qichuan Tian and Zhengguang Liu, “A Practical Iris Recognition Algorithm”, Proceedings of the 2006 IEEE International Conference on Robotics and Biometrics,
  9. 2006, Kunming, China
  10. Peng-Fei Zhang, De-Sheng Li: Qi Wang, “A novel iris recognition method based on feature fusion”, Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai, 2004.
  11. Hao Meng and Cuiping Xu, “Iris Recognition Algorithms Based on Gabor Wavelet Transforms”, Proceedings of the IEEE International Conference on Mechatronics and Automation, 2006, Luoyang, China
  12. Peeranat Thoonsangngam, Somying Thainimit, Vutipong Areekul, “Relative Iris Codes”, International Symposium on Signal Processing and Information Technology, IEEE, 2007
  13. Byungjun Son, Hyunsuk Won, Gyundo Kee, Yillbyung Lee, “Discriminant Iris Feature and Support Vector Machines for iris recognition”, IEEE, International Conference on Image Processing (ICIP), 2004, pp. 864-868.
  14. Xiaofu He, Pengfei Shi, “Extraction of Complex Wavelet Features for Iris Recognition”, IEEE, 2008
  15. Erik Rydgren, Thomas EA, FridCric Amiel, Florence Rossant, Aiara Amara, “Iris Features Extraction Using Wavelet Packets”, International Conference on Image Processing (ICIP), IEEE , 2004
  16. K. Grabowski, W. Sankowski, M. Napieralska, M. Zubert, A. Napieralski, “Iris Recognition Algorithm Optimized For Hardware Optimization”, IEEE, 2006
  17. Chengqiang Liu, Mei Xie, “Iris Recognition Based on DLDA”, IEEE, The 18th International Conference on Pattern Recognition (ICPR'06), 2006
  18. Jing Huang, Xinge You, Yuan Yan Tang, “Iris Recognition Based on Non-separable Wavelet”, IEEE, 2008
  19. T. Acharya and A.K. Ray (2005), “Image Processing: Principles and Applications”, Wiley.
  20. S. Mallat (1989), “A theory for multi-resolution signal decomposition: the wavelet representation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.11, no.7, pp.674-693.
  21. Manisha Nirgude, Sachin Gengaje, “Wavelet based iris recognition system”, IJERT, Vol. 4, Issue 02, Feb 2015.
  22. Steve R. Gunn., “Support Vector Machines for Classification and Regression”, Technical Report, University of Southampton, 1998
  23. Sergios Thedoridis and Konstantinos Kuotroumbas , “Pattern Recognition”, Fourth Edition, Elsevier, Academic Press, 2011
  24. CASIA iris image database. http://www.sinobiometrics.com/casiairis.htm.
  25. K. Saminathan, T. Chakravarthy and M. Chithra Devi, “Iris Recognition Based on Kernels of Support Vector Machine”, ICTACT journal on soft computing: computing theory, application and implications in engineering and technology, January 2015, volume: 05, issue- 02
  26. Himanshu Rai, Anamika Yadav, “Iris recognition using combined support vector machine and Hamming distance approach”, Journal on Expert Systems with Applications 41 (2014), pp. 588–593

Keywords

Iris recognition, Multi-resolution analysis, wavelet transform, support vector machine, RBF kernel