Design and Implementation of Cloud Service System Based on Face Recognition

Face recognition technology can be applied to many aspects in smart city, and the combination of face recognition and deep learning can bring new applications to the public security. The use of deep learning machine vision technology and video-based image retrieval technology can quickly and easily solve the current problem of quickly finding the missing children and arresting criminal suspects. The main purpose of this paper is to propose a novel face recognition method for population search and criminal pursuit in smart cities. In large and medium-sized security, the face pictures of the most similar face images can be accurately searched in tens of millions of photos. The storage requires a powerful information processing center for a variety of information storage and processing. To fundamentally support the safe operation of a large system, cloud-based network architecture is considered and a smart city cloud computing data center is built. In addition, this paper proposed a cloud server architecture for face recognition in smart city environments.

[1]  Liu Yang,et al.  Replication Strategy for Spatiotemporal Data Based on Distributed Caching System , 2018, Sensors.

[2]  Xiao Xiang Zhu,et al.  Deep learning in remote sensing: a review , 2017, ArXiv.

[3]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[4]  Lin Yang,et al.  MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Fotis Foukalas,et al.  Wireless Communication Technologies for Safe Cooperative Cyber Physical Systems , 2018, Sensors.

[6]  Nor Badrul Anuar,et al.  The role of big data in smart city , 2016, Int. J. Inf. Manag..

[7]  Haifeng Li,et al.  Error aware multiple vertical planes based visual localization for mobile robots in urban environments , 2015, Science China Information Sciences.

[8]  Adel Taweel,et al.  Benchmarking and Performance Analysis for Distributed Cache Systems: A Comparative Case Study , 2017, TPCTC.

[9]  Reza Tavakkoli-Moghaddam,et al.  New approach based on group technology for the consolidation problem in cloud computing-mathematical model and genetic algorithm , 2018 .

[10]  Stefan Carlsson,et al.  CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[11]  Gorthi R. K. Sai Subrahmanyam,et al.  Computationally efficient deep tracker: Guided MDNet , 2017, 2017 Twenty-third National Conference on Communications (NCC).

[12]  Eugenio Cesario,et al.  Balancing Speedup and Accuracy in Smart City Parallel Applications , 2016, Euro-Par Workshops.

[13]  Yuan Yao,et al.  Big data in smart cities , 2015, Science China Information Sciences.

[14]  Victoria Lopez,et al.  Big+Open Data: Some applications for a Smartcity , 2015, 2015 IEEE International Conference on Progress in Informatics and Computing (PIC).

[15]  Ljupco Kocarev,et al.  ISO-Standardized Smart City Platform Architecture and Dashboard , 2017, IEEE Pervasive Computing.

[16]  Santwana Sagnika,et al.  Workflow scheduling in cloud computing environment using Cat Swarm Optimization , 2014, 2014 IEEE International Advance Computing Conference (IACC).

[17]  Suresha,et al.  Task-Scheduling in Cloud Computing Environment: Cost Priority Approach , 2018 .