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Arabian Journal for Science and Engineering
Published in: Arabian Journal for Science and Engineering 8/2022

02-02-2022 | Research Article-Computer Engineering and Computer Science

mRMR-PSO: A Hybrid Feature Selection Technique with a Multiobjective Approach for Sign Language Recognition

Authors: Sandhya Rani Bansal, Savita Wadhawan, Rajeev Goel

Published in: Arabian Journal for Science and Engineering | Issue 8/2022

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Abstract

In this paper, a hybrid feature selection technique named mRMR-PSO has been proposed with a multiobjective approach for automatic recognition of sign language. The features are extracted by histogram of oriented gradient (HOG) for input gestures. Here, mRMR is used as a pre-processor for the removal of redundant and irrelevant features reducing the computational burden of PSO. Further, PSO chooses a feature subset having maximum accuracy with minimum features based on the classifier performance. A multi-class support vector machine is used as a classifier. The effectiveness of the proposed approach has been exhaustively tested on seven publically available benchmark datasets for three different sign languages with both uniform and complex backgrounds. The experimental results obtained by mRMR-PSO achieve more accurate classification with reduced feature vector size as compared to HOG (no FS), mRMR, PSO. Furthermore, Friedman’s test has been conducted to show the significance of mRMR-PSO in comparison to others.
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Metadata
Title
mRMR-PSO: A Hybrid Feature Selection Technique with a Multiobjective Approach for Sign Language Recognition
Authors
Sandhya Rani Bansal
Savita Wadhawan
Rajeev Goel
Publication date
02-02-2022
Publisher
Springer Berlin Heidelberg
Published in
Arabian Journal for Science and Engineering / Issue 8/2022
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI
https://doi.org/10.1007/s13369-021-06456-z

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