An Investigation of Student Facial Expression Recognition Based on Machine Learning Algorithms

DOI

10.2991/978-94-6463-370-2_60How to use a DOI?

Keywords

Student Facial Expression; Deep Learning; Learning State

Abstract

Facial Expression Recognition (FER) refers to the classification of facial expressions based on facial expressions in images by using classification algorithms, which are usually classified into 6 categories. Given the large number of students in a classroom, researchers have employed facial expression recognition technology to enhance the recognition of students’ expressions, thereby ensuring the quality of teaching. Teachers can effectively complete classroom teaching by judging students’ learning state in class through students’ expression recognition technology. By providing a comprehensive review of recent literature on student facial expression recognition, this paper aims to provide valuable insights into the application of this technology in education. The first section of this paper provides an introduction to data preparation and preprocessing methods of students’ facial expression recognition methods, including the construction of datasets and feature extraction, then summarizes the models used in facial expression methods and the verification effects of the models including Support Vector Machine (SVM) and Deep Learning-based methods, and final section summarizes the full paper.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yanyi Chen
PY  - 2024
DA  - 2024/02/14
TI  - An Investigation of Student Facial Expression Recognition Based on Machine Learning Algorithms
BT  - Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)
PB  - Atlantis Press
SP  - 589
EP  - 597
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-370-2_60
DO  - 10.2991/978-94-6463-370-2_60
ID  - Chen2024
ER  -