Publication Type : Conference Paper
Publisher : IEEE
Source : 2021 Innovations in Power and Advanced Computing Technologies (i-PACT), Kuala Lumpur, Malaysia, 2021, pp. 1-5
Url : https://ieeexplore.ieee.org/document/8843453
Campus : Chennai
School : School of Engineering
Department : Electronics and Communication
Year : 2021
Abstract : Hand gestures are the most common forms of communication and have great importance in our world. They can help in building safe and comfortable user interfaces for a multitude of applications. Various computer vision algorithms have employed color and depth camera for hand gesture recognition, but robust classification of gestures from different subjects is still challenging. I propose an algorithm for real-time hand gesture recognition using convolutional neural networks (CNNs). The proposed CNN achieves an average accuracy of 98.76% on the dataset comprising of 9 hand gestures and 500 images for each gesture.
Cite this Research Publication : S. Shanmugam, Lakshmanan S A, P. Dhanasekaran, P. Mahalakshmi and A. Sharmila, "Hand Gesture Recognition using Convolutional Neural Network," 2021 Innovations in Power and Advanced Computing Technologies (i-PACT), Kuala Lumpur, Malaysia, 2021, pp. 1-5, doi: 10.1109/i-PACT52855.2021.9696463