Hand Gesture Recognition for Virtual Mouse Control

Abstract

Our work delved into the complexities of real-time hand motion interpretation and fingertip recognition to simulate the functionality of a traditional mouse. We developed a python-based technique that seamlessly translates hand movements into mouse commands by analyzing the angles between fingers and calculating the ratio of the hand’s silhouette to its convex hull. Our methodology was refined to ensure an intuitive and accurate user experience. However, challenges remained in achieving robustness and accuracy in gesture recognition systems in various scenarios, including variations in lighting, hand orientation, and individual human characteristics. These factors had a significant impact on system performance and reliability. To address these challenges, our approach incorporated the algorithms and machine learning models designed to adapt to different conditions. Despite these advances, further research and development were essential to improve the reliability and comprehensiveness of gesture recognition technologies.