Abstract

This project presents a facial expression recognition model designed to recognize and classify emotions in individuals. The model utilizes a database like the Facial Expression Recognition (FER) database, which contains various facial expressions. Our model is proficient at recognizing faces and accurately detecting the emotions they convey, classifying them into seven distinct emotion categories. The primary objective of this model is to accurately identify and classify facial expressions, a valuable tool for applications in emotion analysis and understanding. Nonetheless, the project acknowledges the potential for future expansion. The model serves as a foundational framework for future endeavours in assessing student attentiveness. The recognition of faces and detection of emotions can be integrated into an attentiveness assessment system, providing educators and researchers with insights into the emotional states of students during learning activities.