Hand Gesture Recognition for Game Control Using Camera-Based Sensors

Abstract

Hand gesture recognition has become an important interaction paradigm in human–computer interaction, particularly for gaming applications that require intuitive and immersive control mechanisms. Conventional input devices, such as keyboards and game controllers, often limit natural interaction and accessibility. Vision-based approaches using camera sensors offer a promising alternative by enabling contactless and intuitive game control. However, achieving accurate and real-time hand gesture recognition using low-cost camera-based sensors remains challenging due to variations in lighting conditions, background complexity, and computational constraints. Motivated by the growing demand for responsive and accessible interaction techniques, this study proposes a camera-based hand gesture recognition system designed for real-time game control. The main contribution of this research lies in the development of an integrated recognition pipeline that combines image preprocessing, feature extraction, gesture classification, and gesture-to-control mapping within a unified framework. The proposed system is implemented using RGB image input from a standard camera and evaluated in an interactive gaming environment. Experimental evaluation demonstrates that the system can accurately recognize predefined hand gestures and translate them into responsive game actions while maintaining real-time performance. The results indicate a favorable balance between recognition accuracy, computational efficiency, and interaction responsiveness, confirming the feasibility of the proposed approach for practical gaming applications. Future work will focus on expanding the gesture set, incorporating adaptive learning mechanisms to accommodate user variability, and enhancing robustness for more complex dynamic gestures and deployment scenarios.