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Hand gesture recognition based on a Harris Hawks optimized Convolution Neural Network.
- Source :
-
Computers & Electrical Engineering . May2022, Vol. 100, pN.PAG-N.PAG. 1p. - Publication Year :
- 2022
-
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%. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00457906
- Volume :
- 100
- Database :
- Academic Search Index
- Journal :
- Computers & Electrical Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 157219547
- Full Text :
- https://doi.org/10.1016/j.compeleceng.2022.107836