Deep Learning Based Facial Emotion Recognition for Analysing the Effectiveness of Online Class
Keywords:
Mobile Netv2, Online class, Fine tuning, Performance metric, ImageNet DatasetAbstract
Online classes break down barriers of distance and time, allowing students from different geographical locations and backgrounds to access quality education. However, monitoring student engagement and emotional well-being during online classes presents a unique challenge. This study aims to analyze the facial expressions of students during online classes, in order to assess their emotional states and evaluate the performance of a fine-tuned MobileNet V2 architecture. To conduct this study, we utilized the CK+ dataset, which consists of labeled facial expressions captured in controlled laboratory settings. To specifically identify the emotions shown by students during online classes, the MobileNet V2 model is first pre-trained on ImageNet, a large-scale picture classification dataset, and then refined on the CK+ dataset. Preprocessing techniques such as image augmentation and normalization are applied to enhance the model's generalization capability. Before fine-tuning, the pre-trained model achieved moderate level of performance. After fine-tuning, the performance of the model achieved higher accuracy of 98.40% compared to the base model, indicating its enhanced ability to detect and classify facial emotions during online classes. By leveraging deep learning-based tools like the proposed model, educators can gain valuable real-time feedback on the effectiveness of their online teaching methods and make data-driven decisions to optimize the learning experience for their students.
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