(untitled) (International Journal of Engineering & Extended Technologies Research (IJEETR), 2026)
The exponential growth in screen-based digital devices has generated significant concern regarding the impact of excessive screen exposure on the physical and cognitive health of children. Prolonged screen engagement has been clinically linked to digital eye strain, disrupted circadian rhythms, impaired attention spans, and developmental delays in young users. Existing parental control mechanisms rely predominantly on manual time-limit configurations and coarse application blocking, failing to account for the child’s actual visual engagement with screen content. This paper introduces an AI-Based Eye Detection System that employs computer vision and deep learning techniques to continuously monitor children’s real-time eye interaction with digital screens, enabling intelligent, evidence-based screen time management. The proposed system leverages OpenCV-based facial landmark detection combined with Convolutional Neural Network (CNN) classifiers to distinguish active eye engagement from passive presence, track cumulative screen exposure durations with high temporal granularity, and trigger configurable real-time alerts to notify parents or guardians when predefined safe-use thresholds are exceeded. The system achieves a training accuracy of 98.57% and a validation accuracy of 98.85% across 30 training epochs, demonstrating robust convergence on the eye state classification task. Experimental evaluation confirms that the proposed architecture outperforms conventional timer-based and manual threshold systems in sensitivity, specificity, and adaptability to diverse lighting conditions and facial orientations. Future enhancements include adaptive personalization algorithms that tailor screen time recommendations to individual behavioural usage patterns, multi-device integration, and real-time parent notification via mobile applications.