Smart Classroom Monitoring Using Novel Real-Time Facial Expression Recognition System

Emotions play a vital role in education. Technological advancement computer vision using deep learning models has improved automatic emotion recognition. In this study, real-time recognition system is developed incorporating novel salient facial features for classroom assessment model. The proposed each are initially detected HOG face recognition, and then performed by training convolutional neural network (CNN) that takes input from camera deployed the classroom. will analyze expressions of student during learning. selected emotional states happiness, sadness, fear along with cognitive–emotional satisfaction, dissatisfaction, concentration. tested against variables gender, department, lecture time, seating positions, difficulty subject. contributes to improve