Title: Emotion recognition method for multimedia teaching classroom based on convolutional neural network

Authors: Xiaojuan Tang; Meilin Jin

Addresses: School of Information Science and Engineering, Changsha Normal University, Changsha, Hunan, China ' School of Economics and Trade Management, Wenzhou Vocational College of Science and Technology, Wenzhou, Zhejiang, China

Abstract: In order to further improve the teaching quality of multimedia teaching in school daily teaching, a classroom facial expression emotion recognition model is proposed based on convolutional neural network. VGGNet and CliqueNet are used as the basic expression emotion recognition methods, and the two recognition models are fused while the attention module CBAM is added. Simulation results show that the designed classroom face expression emotion recognition model based on V-CNet has high recognition accuracy, and the recognition accuracy on the test set reaches 93.11%, which can be applied to actual teaching scenarios and improve the quality of classroom teaching.

Keywords: multimedia teaching; emotion recognition; model fusion; convolutional neural network.

DOI: 10.1504/IJWMC.2024.142065

International Journal of Wireless and Mobile Computing, 2024 Vol.27 No.4, pp.393 - 401

Received: 09 Oct 2023
Accepted: 02 Mar 2024
Published online: 07 Oct 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article