Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2014)
Zhu, X.X., Tuia, D., Mou, L., Xia, G.S., Zhang, L., Xu, F., et al.: Deep learning in remote sensing: a review. IEEE Geosci. Remote Sens. Mag. (2017, in press)
Razavian, A.S., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition, pp. 512–519 (2014)
Zhang, Z., Xie, Y., Xing, F., Mcgough, M., Yang, L.: MDNet: a semantically and visually interpretable medical image diagnosis network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3549–3557 (2017)
Venugopal, M.P., Mishra, D., Subrahmanyam, G.R.K.S.: Computationally efficient deep tracker: guided MDNet. In: National Conference on Communications, pp. 1–6 (2017)
Li, D.R., Cao, J.J., Yuan, Y.: Big data in smart cities. Sci. China Inform. Sci. 58(10), 1–12 (2015)
Hashem, I.A.T., Chang, V., Anuar, N.B., Adewole, K., Yaqoob, I., Gani, A., et al.: The role of big data in smart city. Int. J. Inf. Manag. 36(5), 748–758 (2016)
Bilgaiyan, S., Sagnika, S., Das, M.: Workflow scheduling in cloud computing environment using Cat Swarm Optimization. In: IEEE Advance Computing Conference, pp. 680–685 (2014)
Lopez, V., Miñana, G., Sánchez, O., González, B., Valverde, G., Caro, R.: Big + Open data: some applications for a Smartcity. In: IEEE International Conference on Progress in Informatics and Computing, pp. 384–389 (2016)
Mastroianni, C., Cesario, E., Giordano, A.: Balancing speedup and accuracy in smart city parallel applications. In: European Conference on Parallel Processing, pp. 224–235. Springer, Cham (2016)
Zdraveski, V., Mishev, K., Trajanov, D., Kocarev, L.: Iso-standardized smart city platform architecture and dashboard. IEEE Pervasive Comput. 16(2), 35–43 (2017)
Alworafi, M.A., Al-Hashmi, A., Dhari, A., Suresha, Darem, A.B.: Task-scheduling in cloud computing environment: cost priority approach (2018)
Shahdi-Pashaki, S., Teymourian, E., Tavakkoli-Moghaddam, R.: New approach based on group technology for the consolidation problem in cloud computing-mathematical model and genetic algorithm. Comput. Appl. Math. 37(1), 1–26 (2018)
Salhi, H., Odeh, F., Nasser, R., Taweel, A.: Benchmarking and performance analysis for distributed cache systems: a comparative case study. In: Performance Evaluation and Benchmarking for the Analytics Era, pp. 147–163 (2018)
Xiong, L., Yang, L., Tao, Y., Xu, J., Zhao, L.: Replication strategy for spatiotemporal data based on distributed caching system. Sensors 18(1), 1–14 (2018)
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant 61672296, Grant 61602261, Grant 61762071 and Grant 61872194, in part by the Major Natural Science Research Projects in Colleges and Universities of Jiangsu Province under Grant 18KJA520008, and the 1311 Talent Plan of the Nanjing University of Posts and Telecommunications (NUPT).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, H., Xu, H., Li, P. (2020). Design and Implementation of Cloud Service System Based on Face Recognition. In: Barolli, L., Hussain, F., Ikeda, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2019. Advances in Intelligent Systems and Computing, vol 993. Springer, Cham. https://doi.org/10.1007/978-3-030-22354-0_56
Download citation
DOI: https://doi.org/10.1007/978-3-030-22354-0_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22353-3
Online ISBN: 978-3-030-22354-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)