Secondary iris recognition method based on local energy-orientation feature

Journal of Electronic Imaging

Volumn 24, Issue 1, 2015, Pages

  Huo, Guang ^a,b   Liu, Yuanning ^a   Zhu, Xiaodong ^a   Dong, Hongxing ^a  

^a JILIN UNIVERSITY   (China)

^b NORTHEAST ELECTRIC POWER UNIVERSITY   (China)

Author keywords

energy orientation feature; feature extraction; iris recognition; two dimensional Gabor filter

Indexed keywords

BIOMETRICS; EXTRACTION; FEATURE EXTRACTION; GRAPHIC METHODS;

CHI SQUARE DISTANCE; ENERGY ORIENTATION; IRIS RECOGNITION; IRIS RECOGNITION ALGORITHM; LOCAL ENERGY; LOCAL FEATURE;

GABOR FILTERS;

EID: 84923870188     PISSN: 10179909     EISSN: 1560229X     Source Type: Journal    
DOI: 10.1117/1.JEI.24.1.013033     Document Type: Article

References (27)

1. A. K. Jain, A. Ross, and S. Prabhakar, "An introduction to biometric recognition," J. IEEE Trans. Circuits Syst. Video Technol. 14(1), 4-20 (2004).
2. J. Daugman, "How iris recognition works," J. IEEE Trans. Circuits Syst. Video Technol. 14(1), 21-30 (2004).
3. H. Rai and A. Yadav, "Iris recognition using combined support vector machine and Hamming distance approach," J. Exp. Syst. Appl. 41(2), 588-593 (2014).
4. J. Daugman, "New methods in iris recognition," IEEE Trans. Syst. Man, Cyberne. Part B: Cyberne. 37(5), 1167-1175 (2007).
5. M. Nabtiand and A. Bouridane, "An effective and fast iris recognition system based on a combined multiscale feature extraction technique," Pattern Recognit. 41(3), 868-879 (2008).
6. Q. Wang et al., "Adaboost and multi-orientation 2D Gabor-based noisy iris recognition," Pattern Recognit. Lett. 33(8), 978-983 (2012).
7. W. Q. Yuan et al., "Iris recognition algorithm based on extracting stable feature points of sequence images," Chin. J. Sci. Instrum. 32(5), 1069-1076 (2011).
8. G. Huo, Y. N. Liu, and X. D. Zhu, "2D-Gabor filter design and parameter selection based on iris recognition," J. Inf. Comput. Sci. 11(6), 1995-2002 (2014).
9. R. P. Wildes et al., "A machine-vision system for iris recognition," Mach. Vis. Appl. 9(1), 1-8 (1996).
10. J. Thornton, M. Savvides, and B. V. K. V. Kumar, "A Bayesian approach to deformed pattern matching of iris images," IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 596-606 (2007).
11. K. Miyazawa et al., "An efficient iris recognition algorithm using phasebased image matching," in Proc. IEEE Int. Conf. on Image Processing, (ICIP) 2005, pp. II-49-52, IEEE, New Jersey (2005).
12. W.W. Boles and B. Boashash, "A human identification technique using images of the iris and wavelet transform," IEEE Trans. Signal Process. 46(4), 1185-1188 (1998).
13. L. Ma et al., "Efficient iris recognition by characterizing key local variations," IEEE Trans. Image Process. 13(6), 739-750 (2004).
14. Z. Sun, T. Tan, and X. Qiu, "Graph matching iris image blocks with local binary pattern," Lec. Notes Comput. Sci. 3832, 366-372 (2005).
15. M. De Marsico, M. Nappi, and D. Riccio, "Noisy iris recognition integrated scheme," Pattern Recognit. Lett. 33(8), 1006-1011 (2012).
16. H. Proenca and L. A. Alexandre, "Toward noncooperative iris recognition: A classification approach using multiple signatures," IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 607-612 (2007).
17. Q. Wang et al., "Adaboost and multi-orientation 2D Gabor-based noisy iris recognition," Pattern Recognit. Lett. 33(8), 978-983 (2012).
18. D. H. Liu, K. M. Lam, and L. S. Shen, "Optimal sampling of Gabor features for face recognition," Pattern Recognit. Lett. 25(2), 267-276 (2004).
19. M. Lades et al., "Distortion invariant object recognition in the dynamic link architecture," IEEE Trans. Comput. 42(3), 300-311 (1993).
20. JLU Iris Image Database, Version 3.0, http://biis.jlu.edu.cn/ irisdatabase/ (20 March 2014).
21. CASIA Iris Image Database, Version V1, http://www.cbsr.ia.ac.cn/ irisdatabase.htm (16 June 2014).
22. CASIA Iris Image Database, Version V4-Lamp, http://biometrics .idealtest.org (3 March 2014).
23. D. Shifei, Q. Bingjuan, and T. Hongyan, "An overview on theory and algorithm of support vector machines," J. Univ. Electron. Sci. Technol. China 40(1), 2-10 (2011).
24. C. C. Chang and C. J. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol. 2(3), 27 (2011).
25. O. Chapelle et al., "Choosing multiple parameters for support vector machines," Mach. Learning 46(1-3), 131-159 (2002).
26. X. G. Zhang, "Introduction to statistical learning theory and support vector machines," Acta Automat. Sin. 26(1), 32-42 (2000).
27. Y. C. Lee, "Application of support vector machines to corporate credit rating prediction," Exp. Syst. Appl. 33(1), 67-74 (2007).