University of Oulu

Gadekallu, T. R., Srivastava, G., Liyanage, M., M., I., Chowdhary, C. L., Koppu, S., & Maddikunta, P. K. R. (2022). Hand gesture recognition based on a harris hawks optimized convolution neural network. Computers and Electrical Engineering, 100, 107836. https://doi.org/10.1016/j.compeleceng.2022.107836

Hand gesture recognition based on a Harris Hawks optimized Convolution Neural Network

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Author: Gadekallu, Thippa Reddy1; Srivastava, Gautam2,3; Liyanage, Madhusanka4,5;
Organizations: 1School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
2Department of Mathematics and Computer Science, Brandon University, Manitoba, Canada
3Research Centre of Interneural Computing, China Medical University, Taichung, Taiwan
4School of Computer Science, University Collage Dublin, Ireland
5Centre for Wireless Communications, University of Oulu, Finland
Format: article
Version: accepted version
Access: embargoed
Persistent link: http://urn.fi/urn:nbn:fi-fe202301122582
Language: English
Published: Elsevier, 2022
Publish Date: 2024-03-04
Description:

Abstract

Hand gestures are an effective method of communication, especially when we are communicating with people who cannot understand our spoken language. Furthermore, it is a key aspect to human–computer interaction. Understanding hand gestures is very important to ensure that listeners understand what speakers are attempting to communicate. Even though several researchers have proposed deep learning-based models for hand gesture recognition, the hyper-parameter tuning of these models is a relatively unexplored area. In this work, Convolutional Neural Networks (CNN) are used to classify hand gesture images. To tune the hyper-parameters of the CNN, a recently developed metaheuristic algorithm, namely, the Harris Hawks Optimization (HHO) algorithm, is used. Our in-depth comparative analysis proves that the proposed HHO-CNN hybrid model outperforms the existing models by attaining an Accuracy of 100%.

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Series: Computers & electrical engineering
ISSN: 0045-7906
ISSN-E: 1879-0755
ISSN-L: 0045-7906
Volume: 100
Article number: 107836
DOI: 10.1016/j.compeleceng.2022.107836
OADOI: https://oadoi.org/10.1016/j.compeleceng.2022.107836
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
113 Computer and information sciences
Subjects:
Copyright information: © 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
  https://creativecommons.org/licenses/by-nc-nd/4.0/