Volume 11 Number 3 (Jul. 2021)
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IJBBB 2021 Vol.11(3): 65-73 ISSN: 2010-3638
DOI: 10.17706/ijbbb.2021.11.3.65-73

Static and Dynamic Hand Gesture Recognition Using CNN Models

Keyi Wang
Abstract—Similar to the touchscreen, hand gesture based human computer interaction (HCI) is a technology that could allow people to perform a variety of tasks faster and more conveniently. This paper proposes a training method of an imaged-based hand gesture image and video clip recognition system using Convolutional Neural Networks (CNN). A dataset containing images of 6 different static hand gestures is used to train a 2D CNN model. ~98% accuracy is achieved. Furthermore, a 3D CNN model is trained on a dataset containing video clips of 4 dynamic hand gestures resulting in ~83% accuracy. This research demonstrates that a Cozmo robot loaded with pre-trained models is able to recognize static and dynamic hand gestures.

Index Terms—Deep learning, hand gesture recognition, convolutional neural network, human computer interaction (HCI).

Keyi Wang is with 19835 19th Ave. NW, Shoreline, Washington, USA.


Cite:Keyi Wang, "Static and Dynamic Hand Gesture Recognition Using CNN Models," International Journal of Bioscience, Biochemistry and Bioinformatics vol. 11, no. 3, pp. 65-73, 2021.
 

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

ISSN: 2010-3638 (Online)
Abbreviated Title: Int. J. Biosci. Biochem. Bioinform.
Frequency: Quarterly 
DOI: 10.17706/IJBBB
Editor-in-Chief: Prof. Ebtisam Heikal 
Abstracting/ Indexing:  Electronic Journals Library, Chemical Abstracts Services (CAS), Engineering & Technology Digital Library, Google Scholar, and ProQuest.
E-mail: ijbbb@iap.org
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