Image Preprocessing Method Applied on Facial Emotion Recognition Problem
- DOI
- 10.2991/978-94-6463-370-2_32How to use a DOI?
- Keywords
- Facial emotion recognition; Image Preprocessing; Emotional patterns
- Abstract
Sometimes people are unable to accurately express their views on a certain product, which may limit in accurately describing emotions. Facial emotion recognition can help solve the problem of people being unable to accurately express their opinions on products, by analyzing facial expressions to obtain emotional feedback. The application value and importance of this technology lies in providing a nonverbal way to understand user emotions, helping manufacturers enhance user satisfaction. Vision Transformer (ViT) model is very powerful on the Computer Vision Problems. In this paper. I used the VIT model to analyze the facial expression dataset and attempted different preprocessing methods. Through the VIT model. I can associate each data sample with the corresponding expression to determine the category of the expression. Then, compared the results obtained using different preprocessing methods and ultimately determine which method performs best in terms of accuracy. After comparing the accuracy obtained through various preprocessing methods, I found that RandomCrop has the highest accuracy and is most suitable for facial expression.
- 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.
Cite this article
TY - CONF AU - Mingyang Xu PY - 2024 DA - 2024/02/14 TI - Image Preprocessing Method Applied on Facial Emotion Recognition Problem BT - Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023) PB - Atlantis Press SP - 297 EP - 304 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-370-2_32 DO - 10.2991/978-94-6463-370-2_32 ID - Xu2024 ER -