Abstract: Stress among students is a growing concern, impacting academic performance, mental health, and overall well-being. Traditional methods for detecting stress such as self-assessment surveys and physiological measurements are often invasive, subjective, or impractical in real-time educational settings. In recent years, image-based facial expression recognition has emerged as a non-intrusive and efficient approach to detect stress levels using advancements in computer vision and machine learning. This literature survey presents an overview of recent techniques and models developed for stress-level detection through facial expressions, emphasizing their application in student populations. We analyze various datasets, image preprocessing methods, facial emotion recognition algorithms, and stress classification frameworks. The study also identifies current limitations and highlights research gaps to support the development of an improved, real-time, image-based stress detection system for educational institutions.
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DOI:
10.17148/IJARCCE.2025.14491
