Deep-learning-based hand gestures recognition applications for game controls

Abstract

Hand gesture recognition is among the emerging technologies of human computer interaction, and an intuitive and natural interface is more preferable for such applications than a total solution. It is also widely used in multimedia applications. In this paper, a deep learning-based hand gesture recognition sys tem for controlling games is presented, showcasing its significant contributions toward advancing the frontier of natural and intuitive human-computer interac tion. It utilizes MediaPipe to get real-time skeletal information of hand land marks and translates the gestures of the user into smooth control signals through an optimized artificial neural network (ANN) that is tailored for reduced com putational expenses and quicker inference. The proposed model, which was trained on a carefully selected dataset of four gesture classes under different lighting and viewing conditions, shows very good generalization performance and robustness. It gives a recognition rate of 99.92% with much fewer param eters than deeper models such as ResNet50 and VGG16. By achieving high accuracy, computational speed, and low latency, this work addresses some of the most important challenges in gesture recognition and opens the way for new applications in gaming, virtual reality, and other interactive fields.