An Ensemble Deep Learning Approach for Emotion Monitoring System in Online Examinations

Abstract

Around the world, a large number of students experience difficult life situations that have an effect on their emotional and mental health and, ultimately, their academic performance in examinations (exams in short). Emotions have an effect on a student's motivation, focus, and memory, and finally how they perform in exams. An Emotion Monitoring System could be really helpful for understanding how students are feeling during exams and how it affects their overall performance. The contribution of this paper involves in designing a novel facial emotion tracking system which can be used for analyzing facial expressions in real-time thus providing timely emotional support during exams. In this work, we utilized five pretrained deep learning models, namely: DenseNet-121, MobileNetV2, EfficientNet-B0, Inception-V3 and Xception - to classify emotions on processed Emoset dataset. Further, we developed an ensemble model by fusing aforementioned two top-performing deep learning models, thus harnessing the strengths of both models. From the results it can be inferred that ensemble model outperforms the individual pretrained models giving an accuracy of 98.67%. The superior performance of the ensemble models makes it an ideal choice for implementing emotion recognition in real-time applications like Emotion Monitoring in exams. © 2024 IEEE.