Abstract
This article aims to propose a large-scale cloud architecture to serve for biometric system that enrols large population. In identification mode of biometric system, a query template is matched with all stored templates in the database and a match is said to occur with the one with which match-value becomes highest. Hence the identification time = n ×t where n = database size and t = 1:1 matching time. As the database size n becomes sufficiently large, the identification time increases significantly. This leads to long response time of the system. However, achieving the n matching processes in parallel can bring down the total identification system from nt to t. This speeds up the proposed system n times than its sequential counterpart with the trade-off of the cost of resources for cloud and extra communication. The proposed architecture also takes care of threat to compromise secured data as they are passed to different nodes. This architecture passes inputs to cloud nodes hiding the identity-holder’s information so that stealing the identity data of an individual will not compromise the security of the system.
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References
Blanton, M., Zhang, Y., Frikken, K.B.: Secure and Verifiable Outsourcing of Large-Scale Biometric Computations. In: IEEE Third International Conference on Social Computing (SOCIALCOM), pp. 1185–1191 (2011)
Kohlwey, E., Sussman, A., Trost, J., Maurer, A.: Leveraging the Cloud for Big Data Biometrics: Meeting the Performance Requirements of the Next Generation Biometric Systems. In: IEEE World Congress on Services (SERVICES), pp. 597–601 (2011)
Moretti, C., Bui, H., Hollingsworth, K., Rich, B., Flynn, P., Thain, D.: All-Pairs: An Abstraction for Data-Intensive Computing on Campus Grids. IEEE Transactions on Parallel and Distributed Systems 21(1), 33–46 (2010)
Omri, F., Hamila, R., Foufou, S., Jarraya, M.: Cloud-Ready Biometric System for Mobile Security Access. In: Benlamri, R. (ed.) NDT 2012, Part II. CCIS, vol. 294, pp. 192–200. Springer, Heidelberg (2012)
Panchumarthy, R., Subramanian, R., Sarkar, S.: Biometric Evaluation on the Cloud: A Case Study with HumanID Gait Challenge. In: IEEE/ACM Fifth International Conference on Utility and Cloud Computing, pp. 219–222 (2012)
Rosenthal, A., Mork, P., Li, M.H., Stanford, J., Koester, D., Reynolds, P.: Cloud computing: A new business paradigm for biomedical information sharing. Journal of Biomedical Informatics 43(2), 342–353 (2010)
Shelly, Raghava, N.S.: Iris recognition on Hadoop: A biometrics system implementation on cloud computing. In: IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS), pp. 482–485 (2011)
Stojmenovic, M.: Mobile Cloud Computing for Biometric Applications. In: 15th International Conference on Network-Based Information Systems (NBiS), pp. 654–659 (2012)
Yang, J., Xiong, N., Vasilakos, A.V., Fang, Z., Park, D., Xu, X., Yoon, S., Xie, S., Yang, Y.: A Fingerprint Recognition Scheme Based on Assembling Invariant Moments for Cloud Computing Communications. IEEE Systems Journal 5(4), 574–583 (2011)
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Bakshi, S., Raman, R. (2014). Large Scale Cloud for Biometric Identification. In: Kumar Kundu, M., Mohapatra, D., Konar, A., Chakraborty, A. (eds) Advanced Computing, Networking and Informatics- Volume 2. Smart Innovation, Systems and Technologies, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-07350-7_48
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DOI: https://doi.org/10.1007/978-3-319-07350-7_48
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07349-1
Online ISBN: 978-3-319-07350-7
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