STRESS-LEVEL DETECTION IN STUDENTS THROUGH IMAGE-BASED FACIAL EXPRESSION RECOGNITION

Abstract: In the context of modern educational systems, student stress has emerged as a critical issue affecting cognitive performance, emotional health, and academic success. This study introduces an intelligent stress detection framework that leverages image processing and artificial intelligence to identify stress levels in students through facial expression analysis. The system employs Convolutional Neural Networks (CNNs) to automatically extract and interpret visual emotional cues from facial images. Developed as a web-based application using the Flask framework in Python, the solution offers a non-intrusive and real-time assessment tool. The primary objective is to serve as an early warning mechanism, enabling timely interventions to support students' mental well-being and academic resilience.