Quantitative Analysis of Facial Expression Recognition in Classroom Teaching Based on FACS and KNN Classification Algorithm

DOI

10.2991/978-94-6463-012-1_72How to use a DOI?

Keywords

Classroom Teaching; FACS; Adaboost Algorithm; KNN Classification Method; Quantitative Analysis

Abstract

Facial expressions are an important information carrier for individuals to communicate emotionally in the educational system. Through the communication of expressions, teachers and learners can perceive each other’s emotional changes. Learners will unconsciously convey personal thoughts and feelings through facial expressions, and can also identify the attitude and inner world of the other party by observing facial expressions. Expression contains rich behavioural information and is the main way of emotional transmission. As an important direction of individual learning behaviour analysis, facial expression recognition constitutes the basis of emotion understanding and is the premise for computers to understand learners’ emotions. This paper mainly uses the camera in front of the classroom to record the high-definition video of classroom teaching. From the sampled frame images, the AdaBoost algorithm is used to locate and intercept the faces of all students in the classroom, and the images are pre-processed to obtain a 64 × 64 pixel expression area. Gabor and ULBPHS feature fusion, after PCA+dimensionality reduction, combined with KNN classification method for expression classification. Finally, the judgment and output of learning emotions are realized.

Copyright

© 2023 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  - Bing Gong
AU  - Jing Wei
PY  - 2022
DA  - 2022/12/09
TI  - Quantitative Analysis of Facial Expression Recognition in Classroom Teaching Based on FACS and KNN Classification Algorithm
BT  - Proceedings of the 2022 International Conference on Educational Innovation and Multimedia Technology (EIMT 2022)
PB  - Atlantis Press
SP  - 663
EP  - 671
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-012-1_72
DO  - 10.2991/978-94-6463-012-1_72
ID  - Gong2022
ER  -