An optimized Rubber Sheet Model for Normalization Phase of IRIS Recognition

Authors

  • Selvamuthukumaran. S Faculty of Computer Applications, AVC College of Engineering
  • Ramkumar. T School of Information Technology & Engineering, VIT University
  • Shantharajah SP School of Information Technology & Engineering, VIT University

DOI:

https://doi.org/10.34306/csit.v6i1.356

Keywords:

Biometrics, iris normalization, iris recognition, spherical coordinates, histogram equalization

Abstract

Iris recognition is a promising biometric authentication approach and it is a very active topic in both research and realistic applications because the pattern of the human iris differs from person to person, even between twins. In this paper, an optimized iris normalization method for the conversion of segmented image into normalized form has been proposed. The existing methods are converting the Cartesian coordinates of the segmented image into polar coordinates. To get more accuracy, the proposed method is using an optimized rubber sheet model which converts the polar coordinates into spherical coordinates followed by localized histogram equalization. The experimental result shows the proposed method scores an encouraging performance with respect to accuracy.

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References

N. Mojtaba, G. Sedigheh, Iris recognition based on using ridgelet and curvelet transform. International Journal

of Signal Processing, Image Processing and Pattern Recognition. 4 (2) (2011) 7-18.

R.M. Da Costa, A. Gonzaga, Dynamic features for Iris recognition. IEEE Transactions on Systems, Man and

Cybernetics. 42 (4) (2012) 1072-1082.

A.D. Rahulkar, R.S. Holambe, Half-Iris feature extraction and recognition using a new class of biorthogonal

Triplet Half-band filter bank and flexible k-out-of-n: A Postclassifier. IEEE Transactions on Information

Forensics and Security 7 (1) 2012. 230-240.

G. Yang, S. Pang, Y. Yin, Y. Li, X. Li, SIFT based iris recognition with normalization and enhancement.

International Journal of Machine Learning and Cybernetics. 4 (4) (2012) 401-407.

H. Proença, Iris Biometrics: indexing and retrieving heavily degraded data. IEEE Transactions on Information

Forensics and Security. 8 (12) (2013) 1975-1985.

F.A. Santos, F.A. Faria, L.A. Villas, Iris recognition based on local binary descriptors. IEEE Latin America

Transactions. 13 (8) (2015) 2770 – 2775.

S. Selvamuthukumaran, S. Hariharan, T. Ramkumar, Investigation on Iris recognition system adopting

cryptographic techniques, The International Arab Journal of Information Technology. 12 (1) (2015) 1-8.

R. Himanshu, Y. Anamika, Iris recognition using combined support vector machine and hamming distance

approach. Expert Systems with Applications. 41 (2) (2014) 588–593.

Z. Peng, H. Wang, J. Wu, J. Li, An improved Daugman method for Iris recognition. Wuhan University Journal

of Natural Sciences. 20 (3) (2015) 229–234.

A. Bansal, R. Agarwal, R.K. Sharma, Statistical feature extraction based iris recognition system. Sadhana,

Indian Academy of Sciences. 41 (5) (2016) 507-518.

I. Hamouchene, S. Aouat, Efficient approach for Iris recognition. Signal, Image and Video Processing. 10 (7)

(2016) 1361–1367.

J.G. Daugman, How iris recognition works, IEEE Transactions on Circuits and Systems for Video Technology.

(1) (2004) 21–30.

R.P. Wildes, J.C. Asmuth, G.L. Green, S.C. Hsu, R.J. Kolczynski, J.R. Matey, S.E. McBride, A machine vision

system for Iris recognition. Machine Vision and Applications, 9, (1996), pp.1–8.

H.S. Ali, A.I. Ismail, F.A. Farag, F.E. Abd El-Samie, Speeded up robust features for efficient Iris recognition.

Signal, Image and Video Processing. 10 (8) (2016) 1385–1391.

CASIA iris image database (v1.0): The National Laboratory of Pattern Recognition (NLPR). Institute of

Automation, Chinese Academy of Sciences (CAS).

Y. Chen, J. Yang, C. Wang, N. Liu, Multimodal biometrics recognition based on local fusion visual features and

variational bayesian extreme learning machine. Expert Systems with Applications. 64 (2016) 93-103.

Ajewole, M. O., Owolawi, P. A., Ojo, J. S., & Adetunji, R. M. (2020). Assessment of Fog and Rain

Induced-attenuation on Terrestrial FSO Links. APTIKOM Journal on Computer Science and Information

Technologies, 4(1), 37-44. https://doi.org/10.34306/csit.v4i1.86

Dhar, S., & Roy, S. (2020). Mathematical Document Retrieval System Based on Signature Hashing. APTIKOM

Journal on Computer Science and Information Technologies, 4(1), 45-56. https://doi.org/10.34306/csit.v4i1.87

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Published

2021-04-01

How to Cite

S, S., T, R., & SP, S. (2021). An optimized Rubber Sheet Model for Normalization Phase of IRIS Recognition . APTIKOM Journal on Computer Science and Information Technologies, 6(1), 26-35. https://doi.org/10.34306/csit.v6i1.356

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