Hand Gesture-Controlled Media Player with Adaptive user Profiles

The paper presents a gesture-based media control system augmented with adaptive user profiles and the use of computer vision and facial recognition. The gesture control system is developed in Python using OpenCV and MediaPipe so that the user may control multimedia playback without the use of touch, for example through hand gestures. The most unique aspect of the solution is that it provides support for adaptive, multi-user scenarios, through the use of JSON for profile storage. This means that each users' gesture preferences and media use activity is personalized upon facial recognition. The solution framework emphasizes user convenience, hygiene, and inclusivity for differently-abled users or for use in public space scenarios. The paper describes the design methods, implementation modules, and evaluative performance. Our implementation shows that there is a practical and scalable system integrating real-time hand gesture interaction with adaptive behavior using commodity hardware.