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Multi-biometric authentication system using finger vein and iris in cloud computing

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Abstract

Multi biometric system can be used in cloud computing to achieve higher data security. Biometric authentication refers to automated methods used to identify a person by the features such as face, iris, vein, finger print, palm print etc. In this paper we proposed a novel \(C^{2}\) code derived using orientation and magnitude information extracted from finger vein and iris images to improve the authenticating system. The \(C^{2}\) code eliminates feature selection operator reducing the process complexity as it combines the orientation and magnitude information from finger vein and iris image inputs. This methodology can be implemented in a cloud computing environment based biometric authentication system due to its reduced data handling complexity. This reduced data makes the cloud database more secured and authentication possible anytime anywhere using the cloud environment. This \(C^{2}\) code can produce genuine accept rate more than 98.9 %, while false acceptance rate is about 1 \(\times \) 10\(^{-5}\) % and equal error rate is 0.4 %.

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References

  1. Yang, W., Huang, X.: Comparative competitive coding for personal identification by using finger vein and finger dorsal texture fusion. Inf. Sci. 268, 20–32 (2014)

    Article  Google Scholar 

  2. Kumar, A., Prathyusha, K.V.: Personal authentication using hand vein triangulation and knuckle shape. IEEE Trans. Image Process. 18(9), 2127–2136 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  3. Zhang, L., Zhang, L., Zhang, D., Zhu, H.: Online finger-knuckle-print verification for personal authentication. Pattern Recognit. 43(7), 2560–2571 (2010)

    Article  MATH  Google Scholar 

  4. Zhi, L., Song, S.: An embedded real-time finger-vein recognition system for mobile devices. IEEE Trans. Consum. Electron. 58(2)2 (2012)

  5. Proenca, H.: Iris recognition: on the segmentation of degraded images acquired in the visible length. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1502–1516 (2010)

    Article  Google Scholar 

  6. Ramya, V., Vijaya Kumar, P., Palaniappan, B.: A Novel Design of Finger Vein Recognition for Personal Authentication and Vehicle Security. J. Theor. Appl. Inf. Technol. 6(5) (2014)

  7. Yang, J., Zhang, X.: Feature-level fusion of fingerprint and finger-vein for personal identification. Pattern Recognit. Lett. 33(5), 623–628 (2012)

    Article  Google Scholar 

  8. Miura, N., Nagasaka, A., Miyatake, T.: Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification. Mach. Vis. Appl. 15(4), 194–203 (2004)

    Article  Google Scholar 

  9. Yang, J., Zhang, X.: Feature-level fusion of finger print and finger-vein for personal identification. Pattern Recognit. Lett. 33(5), 623–628 (2012)

    Article  Google Scholar 

  10. Tisse, C.L., Martin, L., Torres, L., Robert, M.: Person identification technique using human iris recognition. In: Proc. Vision Interface, pp. 294–299 (2002)

  11. Daugman, J.: High confidence personal identification by rapid video analysis of iris texture. Security Technology, 1992. Crime Countermeasures. In: Proceedings of Institute of Electrical and Electronics Engineers 1992 International Carnahan Conference on. IEEE (1992)

  12. Daugman, J.: How iris recognition works. IEEE Trans. Circuits Syst. Video Technol. 14(1), 21–30 (2004)

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Sanchez-Avila, C., Sanchez-Reillo, R.: Two different approaches for iris recognition using Gabor filters and multi scale zero-crossing representation. Pattern Recognit. 38(2), 231–240 (2005)

    Article  Google Scholar 

  15. Ma, L.: Efficient iris recognition by characterizing key local variations. IEEE Trans. Image Process. 13(6), 739–750 (2004)

    Article  Google Scholar 

  16. Ma, L.: Personal identification based on iris texture analysis. IEEE Trans. Pattern anal. Mach. Intell. 25(12), 1519–1533 (2003)

    Article  Google Scholar 

  17. Kong, W.K. , Zhang, D.: Competitive coding scheme for palm print verification. In: 16th International Conference on Pattern Recognition (ICPR), pp. 520–523 (2004)

  18. Lin, H.Y., Tzeng, W.G.: A secure erasure code-based cloud storage system with secure data forwarding. IEEE Trans. Parallel Distrib. Syst. 23(6), 995–1003 (2012)

    Article  Google Scholar 

  19. Chen, J., Wang, Y., Wang, X.: On-demand security architecture for cloud computing. Computer 45(7), 73–78 (2012)

    Article  Google Scholar 

  20. Yang, W.M., Rao, Q., Liao, Q.M.: Personal identification for single sample using finger vein location and direction coding. Int. Conf. Hand-based Biom. (ICHB) 2011, 1–6 (2011)

    Google Scholar 

  21. Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 25(6), 599–616 (2009)

    Article  Google Scholar 

  22. Jiang, Q., Ma, J., Wei, F.: On the security of a privacy-aware authentication scheme for distributed mobile cloud computing services. IEEE Syst. J. 9(3), 805–815 (2016)

    Google Scholar 

  23. Zissis, D., Lekkas, D.: Addressing cloud computing security issues. Future Gener. Comput. Syst. 28(3), 583–592 (2012)

  24. Wang, Q., Wang, C., Li, J., Ren, K., Lou, W.: Enabling public verifiability and data dynamics for storage security in cloud computing. Comput. Secur.-ESORICS 2009, 355–370 (2009)

    Google Scholar 

  25. Vidyasree, P., Madhavi, G., Viswanadharaju, S., Borra, S.: A bio-application for accident victim identification using biometrics. In: Classification in Bio. Apps, Springer, Cham , pp. 407–447 (2018)

  26. Li, H., Dai, Y., Tian, L., Yang, H.: Identity-based authentication for cloud computing. Cloud Comput. 157–166 (2009)

  27. Nair, V.S., Reshmypriya, G.N., Rubeena, M.M., Fasila, K.A.: Multi-biometric cryptosystem based on decision level fusion for file uploading in cloud. In: Recent Advances in Electronics and Communication Technology (ICRAECT), 2017 International Conference on, pp. 29–32 IEEE (2017)

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Authors and Affiliations

  1. Department of Information Technology, K.L.N. College of Engineering, Sivagangai, 630612, India

    S. Ilankumaran

  2. Department of Information Technology, Thiagarajar College of Engineering, Madurai, 625015, India

    C. Deisy

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  1. S. Ilankumaran
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  2. C. Deisy
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Correspondence to S. Ilankumaran.

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Ilankumaran, S., Deisy, C. Multi-biometric authentication system using finger vein and iris in cloud computing. Cluster Comput 22 (Suppl 1), 103–117 (2019). https://doi.org/10.1007/s10586-018-1824-9

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  • DOI: https://doi.org/10.1007/s10586-018-1824-9

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