Quantitative Analysis of Facial Expression Recognition in Classroom Teaching Based on FACS and KNN Classification Algorithm
- 10.2991/978-94-6463-012-1_72How to use a DOI?
- Classroom Teaching; FACS; Adaboost Algorithm; KNN Classification Method; Quantitative Analysis
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.
- © 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.
Cite this article
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 -