<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the goal of improving the ability of higher education management and student cultivation, this paper applies face recognition technology to higher education management and student cultivation and proposes a new model of digital management and cultivation. By analyzing the recognition process of the face recognition algorithm in face detection and combining the data to describe the deformability of the face, a neural network-based face recognition algorithm is constructed. After inputting the face image data, it passes through several convolutional layers, a linear rectification layer and a pooling layer and finally connects to the fully connected layer so as to achieve the effect of face recognition. The results show that the face recognition technology training state accuracy rate in the 0~3000th generation rises sharply, which can be seen in the neural network in 3500 generations around the rise has gradually leveled off in 5000 generations to reach convergence. Strengthening digital management thinking can improve the management effect to a certain extent and improve the management content so as to achieve the specific management effect.</p>
</abstract>

With the goal of improving the ability of higher education management and student cultivation, this paper applies face recognition technology to higher education management and student cultivation and proposes a new model of digital management and cultivation. By analyzing the recognition process of the face recognition algorithm in face detection and combining the data to describe the deformability of the face, a neural network-based face recognition algorithm is constructed. After inputting the face image data, it passes through several convolutional layers, a linear rectification layer and a pooling layer and finally connects to the fully connected layer so as to achieve the effect of face recognition. The results show that the face recognition technology training state accuracy rate in the 0~3000th generation rises sharply, which can be seen in the neural network in 3500 generations around the rise has gradually leveled off in 5000 generations to reach convergence. Strengthening digital management thinking can improve the management effect to a certain extent and improve the management content so as to achieve the specific management effect.

The Innovative Model of Higher Education Management and Student Training Mechanism in the Internet Era

The Personnel Department of North Sichuan Medical College

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the goal of improving the ability of higher education management and student cultivation, this paper applies face recognition technology to higher...

The Innovative Model of Higher Education Management and Student Training Mechanism in the Internet Era

Data publikacji: 06 gru 2023

Zakres stron: -

Otrzymano: 06 sty 2023

Przyjęty: 16 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01391

Słowa kluczowe
Convolutional layer, Neural network, Face recognition algorithm, Loss function, Higher education management

© 2023 Mingsi Jiang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

The Innovative Model of Higher Education Management and Student Training Mechanism in the Internet Era

Data publikacji: 06 gru 2023

Zakres stron: -

Otrzymano: 06 sty 2023

Przyjęty: 16 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01391

Słowa kluczoweConvolutional layer, Neural network, Face recognition algorithm, Loss function, Higher education management

© 2023 Mingsi Jiang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Convolutional layer, Neural network, Face recognition algorithm, Loss function, Higher education management