Computer Interaction System Based on Face Recognition

Physical disabilities affect millions of individuals worldwide, significantly limiting their ability to interact with conventional computer input devices such as keyboards and mice. This research presents a non-invasive, real- time computer interaction system that enables hands-free cursor control through facial recognition and gesture detection. The proposed system utilizes a standard webcam to capture facial movements, employing the Dlib library for facial landmark detection and OpenCV for image processing. Key metrics including Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) are computed to detect blinks, winks, and mouth movements, which are subsequently mapped to cursor actions using the PyAutoGUI automation library. The system architecture comprises four primary modules: face detection, feature extraction, action detection, and mouse control execution. Experimental results demonstrate that the system achieves approximately 90% accuracy in gesture recognition under optimal lighting conditions, providing an accessible and cost-effective solution for individuals with mobility impairments. This research contributes to the growing field of assistive technology by offering a practical implementation that requires no additional hardware beyond a standard webcam, thereby promoting digital independence and improved quality of life for physically challenged users.