This paper presents a real-time hand gesture recognition system for touchless computer interaction using a standard webcam and Google’s MediaPipe framework. The proposed method detects hand landmarks and maps predefined gesture patterns to common computer operations, including cursor movement, clicking, scrolling, zooming, and system-level controls such as volume and brightness adjustment. The implementation achieves real-time performance on standard laptop hardware and recognizes eleven gestures with high accuracy under practical testing conditions. Unlike approaches that require specialized hardware or controlled environments, the proposed method operates with commodity webcams and remains functional under varying lighting conditions and hand orientations. User evaluation indicates that the system can be learned quickly and may be suitable for applications in healthcare, public computing environments, and accessibility-oriented interaction.

We leverage Google’s MediaPipe framework for robust hand landmark detection and implement a comprehensive gesture vocabulary supporting cursor movement, clicking, scrolling, zoom in and out, and even system controls like volume and brightness adjustment. The system achieves hand tracking at 30+ frames per second on standard laptop hardware and recognizes eleven distinct gestures with 94% accuracy in real-world testing conditions. Unlike existing solutions requiring specialized hardware or constrained environments, our implementation works with commodity webcams under varying lighting conditions and hand orientations. Real user testing with 25 participants demonstrated that users could perform basic computer tasks within 5 minutes of first use, with gesture recognition accuracy improving to 96% after brief familiarization. The system addresses crucial hygiene concerns in shared computing environments—hospitals, public kiosks, classrooms—where touchless interaction prevents disease transmission while maintaining full functionality. Beyond pandemic-driven applications, this technology offers accessibility benefits for users with motor impairments and represents a stepping stone toward natural human-computer interaction paradigms.

Keywords: Hand Gesture Recognition, MediaPipe, Computer Vision, Human-Computer Interaction, Touchless Interface, Virtual Mouse, Real-Time Processing, Accessibility Technology