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Combined Regularized Discriminant Analysis and Swarm Intelligence Techniques for Gait Recognition
In the gait recognition problem, most studies are devoted to developing gait descriptors rather than introducing new classification methods. This paper proposes hybrid methods that combine regularized discriminant analysis (RDA) and swarm intelligence techniques for gait recognition. The purpose of...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7730123/ https://www.ncbi.nlm.nih.gov/pubmed/33261152 http://dx.doi.org/10.3390/s20236794 |
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author | Krzeszowski, Tomasz Wiktorowicz, Krzysztof |
author_facet | Krzeszowski, Tomasz Wiktorowicz, Krzysztof |
author_sort | Krzeszowski, Tomasz |
collection | PubMed |
description | In the gait recognition problem, most studies are devoted to developing gait descriptors rather than introducing new classification methods. This paper proposes hybrid methods that combine regularized discriminant analysis (RDA) and swarm intelligence techniques for gait recognition. The purpose of this study is to develop strategies that will achieve better gait recognition results than those achieved by classical classification methods. In our approach, particle swarm optimization (PSO), grey wolf optimization (GWO), and whale optimization algorithm (WOA) are used. These techniques tune the observation weights and hyperparameters of the RDA method to minimize the objective function. The experiments conducted on the GPJATK dataset proved the validity of the proposed concept. |
format | Online Article Text |
id | pubmed-7730123 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77301232020-12-12 Combined Regularized Discriminant Analysis and Swarm Intelligence Techniques for Gait Recognition Krzeszowski, Tomasz Wiktorowicz, Krzysztof Sensors (Basel) Article In the gait recognition problem, most studies are devoted to developing gait descriptors rather than introducing new classification methods. This paper proposes hybrid methods that combine regularized discriminant analysis (RDA) and swarm intelligence techniques for gait recognition. The purpose of this study is to develop strategies that will achieve better gait recognition results than those achieved by classical classification methods. In our approach, particle swarm optimization (PSO), grey wolf optimization (GWO), and whale optimization algorithm (WOA) are used. These techniques tune the observation weights and hyperparameters of the RDA method to minimize the objective function. The experiments conducted on the GPJATK dataset proved the validity of the proposed concept. MDPI 2020-11-27 /pmc/articles/PMC7730123/ /pubmed/33261152 http://dx.doi.org/10.3390/s20236794 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Krzeszowski, Tomasz Wiktorowicz, Krzysztof Combined Regularized Discriminant Analysis and Swarm Intelligence Techniques for Gait Recognition |
title | Combined Regularized Discriminant Analysis and Swarm Intelligence Techniques for Gait Recognition |
title_full | Combined Regularized Discriminant Analysis and Swarm Intelligence Techniques for Gait Recognition |
title_fullStr | Combined Regularized Discriminant Analysis and Swarm Intelligence Techniques for Gait Recognition |
title_full_unstemmed | Combined Regularized Discriminant Analysis and Swarm Intelligence Techniques for Gait Recognition |
title_short | Combined Regularized Discriminant Analysis and Swarm Intelligence Techniques for Gait Recognition |
title_sort | combined regularized discriminant analysis and swarm intelligence techniques for gait recognition |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7730123/ https://www.ncbi.nlm.nih.gov/pubmed/33261152 http://dx.doi.org/10.3390/s20236794 |
work_keys_str_mv | AT krzeszowskitomasz combinedregularizeddiscriminantanalysisandswarmintelligencetechniquesforgaitrecognition AT wiktorowiczkrzysztof combinedregularizeddiscriminantanalysisandswarmintelligencetechniquesforgaitrecognition |