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A RESTful E-Governance Application Framework for People Identity Verification in Cloud

  • Ahmedur Rahman Shovon
  • Shanto Roy
  • Tanusree Sharma
  • Md WhaiduzzamanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10967)

Abstract

An effective application framework design for e-governance is definitely a challenging task. The majority of the prior research has focused on designing e-governance architecture where people identity verification takes long time using manual verification system. We develop an efficient application framework that verifies peoples identity. It provides cloud based REST API using deep learning based recognition approach and stores face meta data in neural networks for rapid facial recognition. After each successful identity verification, we store the facial data in the neural network if there is a match between 80–95%. This decreases the error rate in each iteration and enhance the network. Finally, our system is compared with the existing system on the basis of CPU utilization, error rate and cost metrics to show the novelty of this framework. We implement and evaluate our proposed framework which allows any organization and institute to verify people identity in a reliable and secure manner.

Keywords

E-Governance Cloud computing RESTful API ID verification 

References

  1. 1.
    Smitha, K.K., Thomas, T., Chitharanjan, K.: Cloud based E-Governance system: a survey. Procedia Eng. 38, 3816–3823 (2012)CrossRefGoogle Scholar
  2. 2.
    Whaiduzzaman, Md., Naveed, A., Gani, A.: MobiCoRE: mobile device based cloudlet resource enhancement for optimal task response. In: IEEE Transactions on Services Computing (2016)Google Scholar
  3. 3.
    Liang, J.: Government cloud: enhancing efficiency of E-Government and providing better public services. In: 2012 International Joint Conference on Service sciences (IJCSS), pp. 261–265. IEEE (2012)Google Scholar
  4. 4.
    Witarsyah, D., Sjafrizal, T., Fudzee, M.D., Farhan, M., Salamat, M.A.: The critical factors affecting E-Government adoption in Indonesia: a conceptual framework. Int. J. Adv. Sci. Eng. Inf. Technol. 7(1), 160–167 (2017)Google Scholar
  5. 5.
    Rao, M.N., Krishna, S.R.: Efficient and ubiquitous software architecture of E-Governance for Indian Administrative Services. Int. J. Adv. Res. Comput. Sci. 4(11), 66–74 (2013)Google Scholar
  6. 6.
    Zissis, D., Lekkas, D.: Securing E-Government and E-Voting with an open cloud computing architecture. Gov. Inf. Q. 28(2), 239–251 (2011)CrossRefGoogle Scholar
  7. 7.
    Zhang, W.J., Chen, Q.: From E-Government to C-Government via cloud computing. In: 2010 International Conference on E-Business and E-Government (ICEE), pp. 679–682. IEEE (2010)Google Scholar
  8. 8.
    Hashemi, S., Monfaredi, K., Masdari, M.: Using cloud computing for E-Government: challenges and benefits. Int. J. Comput. Inf. Syst. Control Eng. 7(9), 596–603 (2013)Google Scholar
  9. 9.
    Cellary, W., Strykowski, S.: E-Government based on cloud computing and service-oriented architecture. In: Proceedings of the 3rd International Conference on Theory and Practice of Electronic Governance, pp. 5–10. ACM (2009)Google Scholar
  10. 10.
    Ling, H., Soatto, S., Ramanathan, N., Jacobs, D.W.: A study of face recognition as people age. In: 2007 IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–8. IEEE (2007)Google Scholar
  11. 11.
    Park, U., Tong, Y., Jain, A.K.: Age-invariant face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 947–954 (2010)CrossRefGoogle Scholar
  12. 12.
    Ling, H., Soatto, S., Ramanathan, N., Jacobs, D.W.: Face verification across age progression using discriminative methods. IEEE Trans. Inf. Forensics Secur. 5(1), 82–91 (2010)CrossRefGoogle Scholar
  13. 13.
    Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1891–1898 (2014)Google Scholar
  14. 14.
    Taigman, Y., Yang, M., Ranzato, M.A., Wolf, L.: Deepface: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708 (2014)Google Scholar
  15. 15.
    Christensen, J.H.: Using RESTful web-services and cloud computing to create next generation mobile applications. In: Proceedings of the 24th ACM SIGPLAN Conference Companion on Object Oriented Programming Systems Languages and Applications, pp. 627–634. ACM (2009)Google Scholar
  16. 16.
    Adamczyk, P., Smith, P.H., Johnson, R.E., Hafiz, M.: Rest and web services: in theory and in practice. In: REST: From Research to Practice, pp. 35–57. Springer, New York (2011).  https://doi.org/10.1007/978-1-4419-8303-9_2CrossRefGoogle Scholar
  17. 17.
    Color FERET Database. https://www.nist.gov/itl/iad/image-group/color-feret-database. Accessed 03 Oct 2017

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ahmedur Rahman Shovon
    • 1
  • Shanto Roy
    • 1
  • Tanusree Sharma
    • 1
  • Md Whaiduzzaman
    • 1
    Email author
  1. 1.Institute of Information TechnologyJahangirnagar UniversityDhakaBangladesh

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