Detecting the Eye Movement to Control the Cursor Movement of the system

A novel approach to cursor control via real-time eye movement that employs a synergistic combination of OpenCV and machine learning approaches. The ability to operate a cursor in a natural and intuitive manner using eye tracking has the potential to revolutionize human-computer interaction, particularly for people with motor limitations or those looking for more natural and efficient interfaces. The suggested method makes use of OpenCV for robust eye detection and tracking, laying the groundwork for precise gaze estimation. Convolutional Neural Networks (CNNs) are trained to recognize different gaze patterns, enabling precise and dependable cursor control. Over time, the machine learning component adapts the system to individual users, optimizing efficiency and improving user experience. This advances human-computer interaction by presenting a unique and practical cursor solution based on real-time eye movement. The combination of OpenCV, deep learning, and machine learning creates a strong and adaptable framework that paves the way for more intuitive and inclusive computing interfaces in a variety of applications.