Research on Optimization of Facial Expression Recognition Algorithm Based on Convolutional Neural Network and Support Vector Machine
Yuchi Yan
Additional contact information
Yuchi Yan: BMW Brilliance Automotive Ltd., China
International Journal of Information Security and Privacy (IJISP), 2025, vol. 19, issue 1, 1-17
Abstract:
Facial expression recognition (FER), as an important branch of computer vision, has made significant progress in recent years thanks to the development of deep learning technology. This article proposes a support vector machine (SVM) facial expression recognition algorithm based on convolutional neural network (CNN) optimization, aiming to improve recognition accuracy and robustness. This method utilizes the advantages of traditional machine learning such as SVM and the automatic feature extraction characteristics of deep learning, proposes a new feature extraction and classification model, and further improves the model performance through data augmentation, hyperparameter optimization, and other means. Experimental verification shows that the algorithm exhibits good recognition accuracy and robustness on multiple publicly available datasets. This study provides new ideas for improving the performance of facial expression recognition systems, which is of great significance for promoting progress in this field.
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJISP.389192 (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:igg:jisp00:v:19:y:2025:i:1:p:1-17
Access Statistics for this article
International Journal of Information Security and Privacy (IJISP) is currently edited by Yassine Maleh
More articles in International Journal of Information Security and Privacy (IJISP) from IGI Global Scientific Publishing
Bibliographic data for series maintained by Journal Editor ().