Webcam Based Eye Tracking for Hands-Free Navigation

Traditional computing devices leverage physical input devices for navigation, which might be challenging for physically disabled people. Webcam based eye tracking for hands-free navigation presents a hands-free human interaction system that enables users to control their computer cursor through eye movements. The system detects the face, eyes, and iris using MediaPipe in real time through a standard webcam and maps these features to screen coordinates, which are then used to control the cursor via PyAutoGUI. A calibration screen with nine reference points on different parts of the screen is used as a training dataset, as the system uses machine learning models for predicting the gaze. Additionally, slight head movements are also accounted for so as not to disrupt gaze tracking. The system also implements gaze-based navigation, like scrolling when dwelling upon the top or bottom of the screen, double blinking for single clicks, and triple blinking for double clicks. Future enhancements of the model can include voice-controlled navigation. The system demonstrates the viability of affordable, camera-based eye tracking as an effective assistive technology for computer accessibility.