International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
p-ISSN: 2395-0072
www.irjet.net
Real Time Hand Gesture Recognition System for Virtual Gaming Applications Harsh M. Desai 1, Abhishek S. Ghodase2, Gaurang D. Ushir3, Megha Patil4 123UG Student, School of Mechatronics Engineering, Symbiosis Skills and Professional University, Maharashtra,
India
4Senior Assistant Professor, School of Mechatronics Engineering, Symbiosis Skills and Professional University,
Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Especially in gaming situations where
Multiple studies has showed various techniques for hand gesture recognition, including traditional computer vision methods and deep learning approach. Techniques such as Haar cascades, Histogram of Oriented Gradients (HOG), and Convolutional Neural Networks (CNNs) have been mostly used for detecting hand gestures from video input.
immersive and simple control techniques are widely appreciated, hand gesture detection has evolved into a major breakthrough in improving human-computer interaction. This work is to design and construct a Python hand gesture based control system for gaming. Using machine learning more specifically, convolutional neural networks, the system seeks to classify motions from real time video input. To increase model accuracy, a varied collection of often-used gaming gestures, swipes, taps, and rotations was gathered and preprocessed.
Python has many libraries and frameworks that support the development of hand gesture recognition systems. The most widely used tool is OpenCV, an open-source library which allows you to process video input and perform computer vision techniques. For training and running deep learning models, there are popular frameworks such as TensorFlow and Pytorch.
Key Words: Hand Gesture detection, Human-Computer Interaction (HCI), Gaming Control System, Real-Time Video Processing, Gesture Classification, Alternative Game Controllers.
In a study by M.C. Parashar et. al. (2020), the researchers developed a real-time hand gesture recognition system using OpenCV and TensorFlow for controlling gaming applications. The authors showed that Python-based tools facilitate the development of gesture recognition systems which provide an accessible and user-friendly experience.
1.INTRODUCTION The gaming landscape has changed forever in the past several years with the advance of how players interact with their games. To make the experience more fluid, intuitive, and immersive, the approach in gaming has changed. One of the more interesting breakthroughs is hand gesture recognition allowing players to control their games purely by natural hand gestures which is a more organic way to interact with the video game.
Access to quality-datasets is paramount for the training and evaluation of hand gesture recognition models. A few publicly accessible datasets include multiple gesturing examples and hand poses to serve these purposes. The American Sign Language (ASL) dataset developed by J. Deng et al. (2018) has become the dataset of choice for researchers investigating hand gesture recognition, as it contains a multitude of hand gesture images related to a given ASL sign. This has provided remarkable resources for training and appraising hand gesture recognition algorithms.
This approach gives us a fresh and dynamic way to connect with virtual environments and potentially changing the way we experience digital entertainment. In this research we explore the potential of gesture based gaming by developing a hand gesture recognition system using Python. Thanks to Python flexibility and the wide range of tools it offers for computer vision and machine learning it provides as the perfect platform for building and experimenting with such interactive systems.
Moreover, the Cha Learn Looking at People (LAP) provides video footage of hand gestures across varied situations, yielding a wide and challenging dataset for gauging the performance of gesture recognition.
Hand gesture recognition for gaming applications has attracted many researchers and developers over the last several years. We outline and establish datasets related to hand gesture gaming, while highlighting studies that look at Python-based approaches and fulfilling the users.
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Impact Factor value: 8.315
Python’s simplicity and readability are strong attributes for fast prototyping and developing hand gesture gaming applications. The expressive syntax and functionality of the libraries available in Python allow rapid implementation and iterations on gesture recognition algorithms.
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