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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2016: Trends and Applications in Knowledge Discovery and Data Mining pp 238–247Cite as

Efficient Iris Image Segmentation for ATM Based Approach Through Fuzzy Entropy and Graph Cut

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9794))

Abstract

In order to realize accurate personal identification in the ATMS, an efficient iris image segmentation approach based on the fuzzy 4-partition entropy and graph cut is presented which can not only yield noisy segmentation results but short the running time. In this paper, an iterative calculation scheme is presented for reducing redundant computations in fuzzy 4-entropy evaluation. Then the presented algorithm uses the probabilities of 4 fuzzy events to define the costs of 4 label assignments (iris, pupil, background and eyelash) for each region in the graph cut. The final segmentation result is computed using graph cut, which produces smooth segmentation result and yields noise. The experimental results demonstrate the presented iterative calculation scheme can greatly reduce the running time. Quantitative evaluations over 20 classic iris images also show that our algorithm outperforms existing iris image segmentation approaches.

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References

  1. Wildes, R.P.: Iris recognition: an emerging biometric technology. J. Proc. IEEE 85(9), 1348–1363 (1997)

    Article  Google Scholar 

  2. Sowjanya, J., Srivani, S.: Iris recognition for ATM based approach. J. IJCER 2(4), 583–585 (2013)

    Google Scholar 

  3. Bechelli, L., Bistarelli, S., Vaccarelli, A.: Biometrics authentication with smartcard. J. Istituto di Informamatica e Teleomatica (ITT) 12, 1–12 (2002)

    Google Scholar 

  4. Cheng, H., Chen, J.R., Li, J.: Threshold selection based on fuzzy c-partition entropy approach. J. Pattern Recogn. 31(7), 857–870 (1998)

    Article  Google Scholar 

  5. Daugman, J.G.: High confidence visual recognition of persons by a test of statistical independence. J. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1148–1161 (1993)

    Article  Google Scholar 

  6. Tsai, C.C., Lin, H.Y., Taur, J., et al.: Iris recognition using possibilistic fuzzy matching on local features. J. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 42(1), 150–162 (2012)

    Article  Google Scholar 

  7. Pundlik, S.J., Woodard, D.L., Birchfield, S.T.: Non-ideal iris segmentation using graph cuts. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2008, pp. 1–6 (2008)

    Google Scholar 

  8. Tang, Y., Mu, W., Zhang, Y., et al.: A fast recursive algorithm based on fuzzy 2-partition entropy approach for threshold selection. J. Neurocomput. 74(17), 3072–3078 (2011)

    Article  Google Scholar 

  9. Tang, Y., Zhang, X., Li, X., et al.: Application of a new image segmentation method to detection of defects in castings. J. Int. J. Adv. Manuf. Technol. 43(5–6), 431–439 (2009)

    Article  Google Scholar 

  10. Peng, B., Zhang, L., Zhang, D., et al.: Image segmentation by iterated region merging with localized graph cuts. J. Pattern Recogn. 44(10), 2527–2538 (2011)

    Article  Google Scholar 

  11. Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. J. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1222–1239 (2001)

    Article  Google Scholar 

  12. Gu, Y., Kumar, V., Hall, L.O., et al.: Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach. J. Pattern Recogn. 46(3), 692–702 (2013)

    Article  Google Scholar 

  13. Otsu, N.: A threshold selection method from gray-level histograms. J. Autom. 11(285–296), 23–27 (1975)

    Google Scholar 

  14. Papamarkos, N., Gatos, B.: A new approach for multilevel threshold selection. J. CVGIP: Graph. Models Image Process. 56(5), 357–370 (1994)

    Google Scholar 

  15. Kullback, S.: Information Theory and Statistics. Courier Corporation, New York (1968)

    MATH  Google Scholar 

  16. Liu, J., Yang, Y.-H.: Multiresolution color image segmentation. J. IEEE Trans. Pattern Anal. Mach. Intell. 16(7), 689–700 (1994)

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the Major Project of National Natural Science Foundation (No. 91218301), the Fundamental Research Funds for Central Universities-Innovation Team Project (No. JBK150503) and the Central Universities-Yong Scholar Development Project (No. JBK150134), the Youth Fund Project of National Natural Science Foundation (No. 61502396). Besides that, this work is also supported by the Internet Financial Innovation and Regulatory Collaborative Innovation Center.

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Authors and Affiliations

  1. School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu, China

    Shibai Yin & Tao Wang

  2. School of Automation, Northwestern Polytechnical University, Xi’an, China

    Yibin Wang

Authors
  1. Shibai Yin
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  2. Yibin Wang
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  3. Tao Wang
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Corresponding author

Correspondence to Shibai Yin .

Editor information

Editors and Affiliations

  1. New Mexico State University , Las Cruces, New Mexico, USA

    Huiping Cao

  2. University of Technology Sydney , Sydney, New South Wales, Australia

    Jinyan Li

  3. Massey University , Auckland, New Zealand

    Ruili Wang

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© 2016 Springer International Publishing Switzerland

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Yin, S., Wang, Y., Wang, T. (2016). Efficient Iris Image Segmentation for ATM Based Approach Through Fuzzy Entropy and Graph Cut. In: Cao, H., Li, J., Wang, R. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9794. Springer, Cham. https://doi.org/10.1007/978-3-319-42996-0_20

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  • DOI: https://doi.org/10.1007/978-3-319-42996-0_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42995-3

  • Online ISBN: 978-3-319-42996-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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