Proceedings of International Conference on Applied Innovation in IT  ·  2026/07/22  ·  Vol. 14  ·  Issue 4  ·  pp. 1245–1252
Computer Vision Syndrome Prevention: Real-Time Detection of Facial Expressions and Eye-to-Screen Distance During Monitor Use
Bekhzod Norboev, Bakhtiyor Nosirov, Bobur Kodirov, Asror Tursunov, Amanov Anuarbek, Samandarova Shakhnoza, Nusratova Shakhzodakhon and Ibotova Sharifa
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Abstract
Computer Vision Syndrome (CVS) refers to a range of visual discomfort symptoms associated with prolonged computer use. This paper presents a real-time webcam-based monitoring system for CVS prevention that combines facial expression recognition and eye-to-screen distance estimation within a single framework. Facial expressions are grouped into two behavioral categories, safe and risk-related, where sadness and fear are treated as proxy behavioral indicators of possible visual discomfort during prolonged screen interaction rather than as clinical diagnostic marker. In parallel, the system estimates eye-to-screen distance using MediaPipe Face Mesh and a focal-length-based calibration procedure to detect potentially unsafe viewing distances. Face detection is performed using CascadeClassifier, while facial expression classification is carried out using a regularized Xception model. The system generates real-time warning notifications through a Tkinter-based interface when risk-related expressions persist above a predefined threshold or when the viewing distance falls below 40 cm. Experimental results for the binary behavioral classification task achieved 94% accuracy, 95% precision, 94% recall, and 94% F1-score. The proposed framework offers a lightweight and practical solution for behavioral CVS risk awareness using only a standard webcam, although broader validation on larger user groups remains necessary.
Keywords
Computer Vision Syndrome Recognition of Facial Expressions Prevention of Digital Eye Strain Estimation of Eye-To-Screen Distance Real-Time Monitoring Systems Human-Computer Interaction Deep Learning Techniques Xception Model
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