Hand Gesture Gaming

Author(s)	Kanchan Narware, Abhishek Dupalli, Akib Ali, Pavan Dhote, Vishal Suryawanshi, Prathamesh Falke
Country	India
Abstract	Hand gesture recognition has emerged as a promising interface for interactive gaming applications, offering intuitive and immersive experiences. This research paper explores the development and implementation of a hand gesture recognition system using Python for gaming purposes. The study investigates various machine learning techniques, particularly convolutional neural networks (CNNs), for accurate detection and classification of hand gestures from video input. The research methodology involves collecting a diverse dataset of hand gesture samples, encompassing common gaming gestures such as swipes, taps, and rotations. Preprocessing techniques are employed to enhance the quality and consistency of the dataset. Subsequently, deep learning architectures, including CNNs, are trained on the dataset to recognize and classify hand gestures in real-time.
Keywords	-
Field	Engineering
Published In	Volume 6, Issue 2, March-April 2024
Published On	2024-03-24
Cite This	Hand Gesture Gaming - Kanchan Narware, Abhishek Dupalli, Akib Ali, Pavan Dhote, Vishal Suryawanshi, Prathamesh Falke - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.15884
DOI	https://doi.org/10.36948/ijfmr.2024.v06i02.15884
Short DOI	https://doi.org/gtn3tm

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