Real-Time Video Control via Hand and Eye Movements Using OpenCV and Mediapipe

This paper focuses on computer vision improvements achieved in hand gesture recognition for human-computer interface and eye detection for sign language translation. Therefore, the ultimate motive of this system is to give a good foundation that will enable detection of the hand and eye movement and then use the said movement to control media players. We are therefore able to decipher their communications by observing other non-verbal cues such as hand and eye motion. In this case, there will be no need for an intermediary as our created system can interface coherently by just employing hand movements and gazing. The aim of the present research is designing an interface that can easily identify and interpret hand movements and eye actions. Through complex systems involving computer vision our systems identify and interpret these gestures, enhancing the ability to easily manage media players. Combined with proper and highly sensitive hand tracking as well as eye detection, our system guarantees the accurate and immediate reading of the user's gestural and facial responses. This can serve to describe the interaction with media player functionality such as play, pause, volume control, etc, alone. The system has significant possibilities of making an integrated and optimized interaction between human and a computer possible. Thus, that will do away with the challenges faced by an individual, which includes allowing him or her to freely communicate as well as to directly interact with designated media players through natural gestures.