TY - GEN
T1 - A RESTful E-Governance Application Framework for People Identity Verification in Cloud
AU - Shovon, Ahmedur Rahman
AU - Roy, Shanto
AU - Sharma, Tanusree
AU - Whaiduzzaman, Md
N1 - Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85049345591&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049345591&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-94295-7_19
DO - 10.1007/978-3-319-94295-7_19
M3 - Conference contribution
AN - SCOPUS:85049345591
SN - 9783319942940
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 281
EP - 294
BT - Cloud Computing – CLOUD 2018 - 11th International Conference, Held as Part of the Services Conference Federation, SCF 2018, Proceedings
A2 - Luo, Min
A2 - Zhang, Liang-Jie
PB - Springer Verlag
T2 - 11th International Conference on Cloud Computing, CLOUD 2018 Held as Part of the Services Conference Federation, SCF 2018
Y2 - 25 June 2018 through 30 June 2018
ER -