Deep Insights: Elevating Academic Performance Through Facial Expression Classification with Advanced Deep Learning Techniques

Facial expressions play a vital role in academics by influencing student engagement and teacher-student interactions. The identification and classification of facial images during classroom learning offer a promising method to measure students’ academic performance. This system holds significant potential benefits for educators, teachers, policy makers, and parents, enabling them to take early steps to enhance students’ academic progress. Among the four deep learning algorithms, utilized for facial emotion expression extraction and detection, namely CNN, VGG-16, ResNet-50, and MobileNet, CNN stands out as the top-performing method. With an impressive accuracy of 87.56% and a validation score of 61.58%, CNN's results reinforce its effectiveness in assessing students’ academic achievements through facial expressions. This innovative approach has the potential to revolutionize how academic performance is monitored and supported, fostering better educational outcomes for students.