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An Efficient Person Authentication method based on the Extraction of Retinal Bifurcations

L. Latha, Dr. S. Thangasamy

Abstract


An efficient method for person authentication based on the retinal blood vessel pattern is presented in this paper. The method initially involves a segmentation process to identify blood vessel intersection points in the retina, then the generation of template consisting of the bifurcation points in the blood vessels and finally matching of the intersection points in two different patterns. This approach differs from previously known methods, in that it uses a retinal minucode centered feature extraction algorithm based on the bifurcation points present in retina. Then the number of matched blood vessel intersection points between the two patterns is used to quantify the degree of matching. The validity of our approach is verified with the experimental results obtained using the publicly available databases, namely DRIVE and STARE. We found that the proposed retinal recognition method gives 100% and 90.1% recognition rates respectively for the above databases.

Keywords


Blood Vessels, Bifurcation Points, Feature Extraction, Retinal Recognition

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References


R.B.Hill, “Retinal identification, in Biometrics: Personal Identification in Networked Society”, A.Jain, R.Bolle, and S.Pankati, Eds., p.126, Springer, Berlin, Germany, 1999

C.Marino, M.G.Penedo, M.Penas, M.J.Carreira, F.Gonzalez, ”Personal authentication using digital retinal images”, Pattern Analysis and Applications (2006) 9: 21–33.

C.Marino, M.G.Penedo, M.J.Carreira,F.Gonzalez, “Retinal angiography Based Authentication, in: Lecture Notes in Computer Science”, vol.2905, Springer, Berlin, 2003, pp.306–313.

Daugman JG (1993) High confidence visual recognition of persons by a test of statistical independence. IEEE Trans PAMI 15(11):1148–1161

Jain A, Hong L, Pankanti S, Bolle R. An identity authentication system using fingerprints. Proc IEEE 85(9)

Zunkel R (1999) Hand geometry based verification, biometrics: personal identification in networked society, Ch. 4. Kluwer, Dordrecht, pp 87–101

Zhao W, Chellappa R, Rosenfeld A, Phillips P (2000) Face recognition: a literature survey, Technical report, National Institute of Standards and Technology

Bunney C (1997) Survey: face recognition systems. Biometric Technol Today 5(4):8–12

Campbell JP (1999) Speaker recognition, biometrics: personal identification in networked society, Ch. 8. Kluwer, Dordrecht, pp 165–189

Verlinde P, Chollet G, Acheroy M (2000) Multi-modal identity verification using expert fusion. Inform Fusion 1(1):17–33

Brunelli R, Falavigna D (1995) Personal identification using multiple cues. IEEE Trans PAMI 17(10):955–966

M.Ortega, C.Marino, M.G. Penedo, M.Blanco, F.Gonzalez, “Biometric Authentication Using Digital Retinal Images,” in Proceedings of the 5th WSEAS International Conference on Applied Computer Science (ACOS 06), pp.422427, Hangzhou, China, April 2006.

Zana F, Klein J-C. “Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation”, IEEE Trans Image Process 2001;10:1010–9.

Yongping Zhang, Wynne Hsu, Mong Li Lee, “Detection of Retinal Blood Vessels Based on Nonlinear Projections”, Journal of Signal Processing Systems (2009) 55:103-112.

Can A, Shen A, Turner J, Tanenbaun H, Roysam B. “Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms”, IEEE Trans Info Technol Biomed 1999; 2:125–38.

Chaudhuri S, Chatterjee S, Katz N, Nelson M, Goldbaum M. “Detection of blood vessels in retinal images using two-dimensional matched filters”, IEEE Trans Med Imag 1989;8:263–9.

Zana F, Klein JC. Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans Imag Proc 2001;10(7):1010–8.

Miles EP, Nuttall AL. Matched filter estimation of serial blood vessel diameters from video images. IEEE Trans Med Imag 1993;12:147–52.

Hoover A, Kouznetsova V, Goldbaum M. “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response”, IEEE Trans Med Imag 2000;19:203–10.

M.Ortega, M.G.Penedo, J.Rouco, N.Barreira, M.J.Carreira, ”Retinal verification using a Feature Points-based Biometric pattern”, EURASIP Journal on Advances in Signal Processing, vol. 2009, Article ID 235746, 13 pages

A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, “An identity authentication system using fingerprints,” Proceedings of the IEEE, vol. 85, no. 9, pp. 1365–1388, 1997.

X. Tan and B. Bhanu, “A robust two step approach for fingerprint identification,” Pattern Recognition Letters, vol. 24,no. 13, pp. 2127–2134, 2003.

Marcos Ortega, M.G.Penedo, J.Rouco, N.Barreira, M.J.Carreira, “Personal verification based on extraction and characterization of retinal feature points”, Journal of Visual Languages and Computing, pages 80-90, s2009.

H. Farzin, H. Abrishami-Moghaddam, and M.-S. Moin, “A novel retinal identification system,” EURASIP Journal on Advances in Signal Processing, vol. 2008, Article ID 280635, 10 pages, 2008.

M. Ortega, C. Mari˜no, M. G. Penedo, M. Blanco, and F.Gonz´alez, “Personal authentication based on feature extraction and optic nerve location in digital retinal images,” WSEAS Transactions on Computers, vol. 5, no. 6, pp. 1169–1176, 2006.

Muhhamad Nazrul Islam, Md. Amran Siddiqui and Samiron Paul, “An Efficient Retina Pattern Recognition Algorithm (RPRA) towards Human Identification”, 2nd International Conference on Computer, Control and Communication, IC4 2009, pp.1 – 6, 2009.

Jinyang shi, Zhiyang you, Ming gu and Kwok-yan lam, “Biomapping: Privacy TrustWorthy Biometrics Using Noninvertible and Discriminable Constructions”, 19th International Conference on Pattern Recognition (ICPR 2008), December 8-11, 2008, Tampa, Florida, USA. IEEE 2008

Chambolle, A. (2004). “An algorithm for total variation minimization and applications. Journal Mathematical Imaging and Vision, 20, 89–97.

Aujol, J. F., Gilboa, G., Chan, T., & Osher, S. (2006). Structure texture image decomposition—Modeling, algorithms, and parameter selection. International Journal of Computer Vision, 67(1), 111–136.

Aujol, J. F., Aubert, G., Blanc-Féraud, L., & Chambolle, A. (2005). Image decomposition into a bounded variation component and an oscillating component. Journal of Mathematical Imaging and Vision, 22(1), 71–88.

Vese, L., & Osher, S. (2003). Modeling textures with total variation minimization and oscillating patterns in image processing. Journal Scientific Computing, 9, 553–572.

J.J. Staal, M.D. Abramoff, M. Niemeijer, M.A. Viergever, B. van Ginneken, "Ridge based vessel segmentation in color images of the retina", IEEE Transactions on Medical Imaging, 2004, vol. 23, pp. 501-509.

STARE project website, http://www.ces.clemson.edu/~ahoover/stare.

Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins (2004). Digital Image Processing using MATLAB. Prentice Hall


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