Intelligent education management system design for universities based on MTCNN face recognition algorithm-Reference-Cited by-同舟云学术

Intelligent education management system design for universities based on MTCNN face recognition algorithm

Published:2023-12-30 Issue:1 Volume:9 Page:
ISSN:2444-8656
Container-title:Applied Mathematics and Nonlinear Sciences
language:en
Short-container-title:

Author:

Xiao Xiaohua¹,Su Zhixian¹,Ye Qiqi²,Qin Zhixia³,Wu Lijie¹

Affiliation:

1. 1 Department of Science and Technology Development , Zhejiang College of Security Technology , Wenzhou , Zhejiang , , China .

2. 2 School of Literature and Communication , Wenzhou University of Technology , Wenzhou , Zhejiang , , China .

3. 3 Department of Scientific Research and Local Cooperation , Wenzhou Business College , Wenzhou , Zhejiang , , China .

Abstract

Abstract This paper first constructs an intelligent education management system for universities according to their needs and explains its logical and technical architecture. Secondly, a multi-task convolutional neural network with MTCNN is used to detect faces, and different loss functions are selected to ensure recognition accuracy. Finally, the effectiveness of the application of intelligent education management systems in universities is verified through performance tests. The results show that the time consumed for face recognition detection using MTCNN is 524ms, and the response time of each function of the system does not exceed 500ms when the number of concurrent users is less than 500, which indicates that the intelligent education management system based on MTCNN face recognition algorithm has a good response speed and meets the requirements of intelligent education management in colleges and universities.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Engineering (miscellaneous),Modeling and Simulation,General Computer Science

Link

https://www.sciendo.com/pdf/10.2478/amns.2023.2.01712

Reference20 articles.

1. Guo, X. R., Li, X., & Guo, Y. M. (2021). Mapping knowledge domain analysis in smart education research. Sustainability, 13.

2. A, Y. G., F, J. H. A., B, M. X., C, Z. W., B, X. H., & D, J. W., et al. (2022). Facial expressions recognition with multi-region divided attention networks for smart education cloud applications. Neurocomputing, 493, 119-128.

3. Psannis, K. E., Gupta, B. B., & Chui, J. K. T. (2021). Wireless communications and mobile computing special issue on the role of ai, ml, and iot in smart education/learning in wireless environments. Wireless Communications and Mobile Computing.

4. Zhang, X., & Chen, L. (2021). College english smart classroom teaching model based on artificial intelligence technology in mobile information systems. Mobile Information Systems, 2021(2).

5. Wu, P. J., Hou, H. Y., & Huang, C. C. (2021). Applying talent quality-management system (ttqs) to enhance information literacy, learning motivation, and computational thinking competency of nursing undergraduates. Sustainability, 13(12), 6528.