Facial Emotion Recognition of Students using Deep Convolutional Neural Network - et. al., K.Nithiyasree, (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Understanding the emotions of the students in a classroom lecture will improve the Teaching – Learning process. It is difficult to find out the students’ interest in a conventional classroom lecture. Hence, we are providing a solution by analyzing the facial emotions of the students in a classroom with the help of a deep learning network. Deep learning is an advancement of machine learning technique which gives more accurate results than the machine learning algorithms. The proposed system will give an accurate prediction with the help of deep convolutional neural networks using VGG-16 architecture. This computer vision model will analyze every individual student’s emotion from the live video taken from the video camera fixed in the classroom and provide the overall emotion of the class based on the highest probability of students’ emotion. Later, our proposed VGG-16 architecture will be compared with models such as Alexnet and Resnet architecture to ensure its better accuracy.