Enhancing Human-Computer Interaction Through Vision-Based Hand Gesture Recognition

Abstract

This study addresses the growing significance of hand gesture recognition systems in fostering efficient human-computer interaction. Despite their versatility, existing visual systems encounter challenges in diverse environments due to lighting and background complexities. With rapid advancements in computer vision, the demand for robust human-machine interaction intensifies. Hand gestures, as expressive conveyors of information, find applications in various domains, including robot control and intelligent furniture. To overcome limitations, the authors propose a vision-based approach leveraging OpenCV and Keras to construct a hand gesture prediction model. This dataset is comprehensive, encompassing all requisite gestures for optimal system performance. The chapter demonstrates the precision and accuracy of the proposed model through validation, showcasing its potential in real-world applications. This research contributes to the broader landscape of enhancing human-computer interaction through accessible and reliable hand gesture recognition systems.