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

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.

[1]  Dimitrios Zissis,et al.  Securing e-Government and e-Voting with an open cloud computing architecture , 2011, Gov. Inf. Q..

[2]  S. Rama Krishna,et al.  Efficient and Ubiquitous Software Architecture of e-Governance for Indian Administrative Services , 2013 .

[3]  Ralph E. Johnson,et al.  REST and Web Services: In Theory and in Practice , 2011, REST: From Research to Practice.

[4]  Xiaogang Wang,et al.  Deep Learning Face Representation from Predicting 10,000 Classes , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Tony Thomas,et al.  Cloud Based E-Governance System: A Survey , 2012 .

[7]  Abdullah Gani,et al.  MobiCoRE: Mobile Device Based Cloudlet Resource Enhancement for Optimal Task Response , 2018, IEEE Transactions on Services Computing.

[8]  Stefano Soatto,et al.  A Study of Face Recognition as People Age , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[9]  Stefano Soatto,et al.  Face Verification Across Age Progression Using Discriminative Methods , 2010, IEEE Transactions on Information Forensics and Security.

[10]  Wenjun Zhang,et al.  From E-government to C-government via Cloud Computing , 2010, 2010 International Conference on E-Business and E-Government.

[11]  Mohammad Masdari,et al.  Using Cloud Computing for E-Government: Challenges and Benefits , 2013 .

[12]  Jian Liang,et al.  Government Cloud: Enhancing Efficiency of E-Government and Providing Better Public Services , 2012, 2012 International Joint Conference on Service Sciences.

[13]  Jason H. Christensen,et al.  Using RESTful web-services and cloud computing to create next generation mobile applications , 2009, OOPSLA Companion.

[14]  Mohd Farhan Md Fudzee,et al.  The Critical Factors Affecting e-Government Adoption in Indonesia: A Conceptual Framework , 2017 .

[15]  Yiying Tong,et al.  Age-Invariant Face Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Wojciech Cellary,et al.  e-government based on cloud computing and service-oriented architecture , 2009, ICEGOV '09.