Affordable Eye-Controlled Mouse: Enhancing Accessibility with Mediapipe and OpenCV

This work introduces a new eye-controlled mouse technology that enables hands-free human-computer interaction. For people with physical restrictions, the system offers a useful and reasonably priced substitute by utilizing Mediapipe's advanced face mesh detection, OpenCV, and PyAutoGUI. The system includes features like fatigue monitoring, blink-based click recognition, and fluid cursor transitions to enhance usability. It transforms eye movements in real time into cursor control. Extensive testing demonstrated dependable performance with excellent responsiveness and mouse tracking accuracy across a variety of user profiles and lighting conditions. This method maintains competitive performance at a large cost reduction as compared to conventional hardware-dependent systems. User comments also underline how the approach has the ability to transform technology accessibility, particularly for those who have mobility challenges. Future development will focus on improving scalability, integrating state-of-the-art machine learning models, and offering multi-monitor capability. This study highlights how combining computer vision and human-computer interaction technologies can democratize access and inclusivity in digital interfaces.