From Facial Expressions to Personalized Learning: Unveiling the Opportunities and Challenges of SVM-Based E-Learning Systems

الملخص

Facial expression recognition (FER) using Support Vector Machines (SVMs) offers intriguing possibilities for distance education and e-learning. Imagine gauging student engagement through expressions like boredom or attentiveness, providing emotional support based on frustration, or even personalizing lectures based on individual needs. Here's where SVMs come in their effectiveness in image recognition and real-time processing make them suitable for analyzing video streams in e-learning platforms. However, challenges like privacy concerns, accuracy limitations, and technical hurdles require careful consideration. While ethically implemented FER systems could lead to improved engagement, personalized learning, and early intervention, further research is necessary to address these limitations and ensure responsible use of such technology. This research paper presents the Human Facial Expression Recognition based on Supervised Learning model (SVM) using a popular dataset Ck+. Our motive was getting best accuracy by using SVM Model with Ck+ dataset and with Fer2013 dataset and eventually succeeded but we also tried combination of Ck+ and Fer2013 dataset with SVM model for experiment. These models provided 50.53% accuracy with Fer2013, 72.61% with combination of Fer2013 and CK+ & 90.96% by using CK+ dataset, providing the most accurate results. Facial expression is an important medium of non-verbal communication. It is a rapidly growing field of research in the domain of Computer Vision and Artificial Intelligence. Since SVM performs better than other existing techniques that are used for facial recognition, therefore improves the overall efficiency of the facial expression recognition.