Abstract

The aim of this study is proposed a new IRS using hybrid methods. These methods used to extract features of tested eye images. Gabor wavelet and Zernike moment used to extract features of iris. Canny edge detection and Hough transform used to determine the iris. The proposed system tested on CASIA-v4.0 interval database. The results show that the proposed method having good accuracy about 97%. PSNR applied on the training and testing iris image to measure the simmilarity between them. PSNR is support the proposed system where, highest value of PSNR for the tesed image dells with the image is belong to the same person in training database.

Keywords:

Biometric, features extraction, Gabor wavelet, iris recognition, Zernike moment and hybrid,

References

1. Jain, A.K., A. Ross and S. Prabhakar, 2004. An introduction to biometric recognition. IEEE T. Circ. Syst. Vid., 14(1): 4-20.
   CrossRef    
2. Abiyev, R.H. and K. Altunkaya, 2008. Personal Iris recognition using neural network. Int. J. Secur. Appl., 2(2): 41-50.
   Direct Link
3. Arora, S., D. Bhatcharjee, M. Nasipuri and L. Malik, 2008. A two stage classification approach for handwritten Devnagari characters. Proceeding of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA, 2007). Sivakasi, Tamil Nadu, India, pp: 399-403.
   Direct Link
4. Arora, S., D. Bhattacharjee, M. Nasipuri, D.K. Basu, M. Kundu and L. Malik, 2009. Study of different features on handwritten Devnagari character. Proceeding of the 2nd International Conference on Emerging Trends in Engineering and Technology (ICETET). Nagpur, India, pp: 929-933.
   CrossRef    
5. Arora, S., D. Bhattacharjee, M. Nasipuri, D.K. Basu and M. Kundu, 2010. Recognition of non-compound handwritten Devnagari characters using a combination of MLP and minimum edit distance. Int. J. Comput. Sci. Secur., 4(1): 107-120.
   Direct Link
6. Arvacheh, E.M., 2006. A study of segmentation and normalization for iris recognition systems. M.Sc. Thesis, University of Waterloo, Ontario, Canada.
   Direct Link
7. Chong, C.W., P. Raveendran and R. Mukundan, 2003. Translation invariants of Zernike moments. Pattern Recogn., 36(8): 1765-1773.
   CrossRef    
8. Darabkh, K.A., R.T. Al-Zubi and M.T. Jaludi, 2014a. New recognition methods for human iris patterns. Proceeding of the 37th International Convention on Information and Communication Technology, Electronics and Microelectronics, (MIPRO, 2014), Opatija, Croatia, pp: 1187-1191.
   CrossRef    
9. Darabkh, K.A., R.T. Al-Zubi, M.T. Jaludi and H. Al-Kurdi, 2014b. An efficient method for feature extraction of human iris patterns. Proceeding of the IEEE 11th International Multi-Conference on Systems, Signals and Devices (SSD), Barcelona, Spain.
   CrossRef    
10. Das, A., 2011. Recognition of human iris patterns. B.Tech., Thesis, Department of Computer Science and Engineering, National Institute of Technology, Rourkela, India.
    
11. Daugman, J.G., 1993. High confidence visual recognition of persons by a test of statistical independence. IEEE T. Pattern Anal., 15(11): 1148-1161.
    CrossRef    
12. Daugman, J., 2004. How iris recognition works. IEEE T. Circ. Syst. Vid., 14(1): 21-30.
    CrossRef    
13. Duda, R.O., P.E. Hart and D.G. Stork, 2000. Pattern Classification. 2nd Edn., Wiley-Interscience, pp: 680.
    PMid:10633101 PMCid:PMC94330    
14. Hwang, S.K. and W.Y. Kim, 2006. A novel approach to the fast computation of Zernike moments. Pattern Recogn., 39(11): 2065-2076.
    CrossRef    
15. Karbhari, V.K., P.D. Deshmukh, S.V. Chavan, M.M. Kazi and Y.S. Rode, 2014. Zernike moment feature extraction for handwritten devanagari (Marathi) compound character recognition. Int. J. Adv. Res. Artif. Intell., 3(1): 68-76.
    Direct Link
16. Kim, H.J. and W.Y. Kim, 2008. Eye detection in facial images using Zernike moments with SVM. ETRI J., 30(2): 335-337.
    CrossRef    
17. Lee, T.S., 1996. Image representation using 2d Gabor wavelets. IEEE T. Pattern Anal., 18(10): 959-971.
    CrossRef    
18. Lokhande, S. and V.N. Bapat, 2013. Wavelet packet based iris texture analysis for person authentication. Signal Image Process. Int. J., 4(2): 91-104.
    CrossRef    
19. Mohammed, S.J., 2014. Iris recognition system using statistical measurement. M.Sc. Thesis, University of Technology, Iraq.
    
20. Patil, P.S., S.R. Kolhe, R.V. Patil and P.M. Patil, 2012. The comparison of iris recognition using principal component analysis, log gabor and gabor wavelets. Int. J. Comput. Appl., 43(1): 29-33.
    
21. Sahmoud, S.A., 2011. Enhancing iris recognition. M.Sc. Thesis, Department of Computer Engineering, Faculty of Engineering, Islamic University, Palestine.
    Direct Link

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.