Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion

Human Gait Recognition (HGR) is a biometric technique that has been utilized for security purposes for the last decade. The performance of gait recognition can be influenced by various factors such as wearing clothes, carrying a bag, and the walking surfaces. Furthermore, identification from differi...

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Autores principales:	Faizan Saleem, Muhammad Attique Khan, Majed Alhaisoni, Usman Tariq, Ammar Armghan, Fayadh Alenezi, Jung-In Choi, Seifedine Kadry
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	gait recognition biometric data augmentation deep learning features optimization features fusion Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/2c45450fa5d14d32b720daf79b9beb0c
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id	oai:doaj.org-article:2c45450fa5d14d32b720daf79b9beb0c
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spelling	oai:doaj.org-article:2c45450fa5d14d32b720daf79b9beb0c2021-11-25T18:57:37ZHuman Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion10.3390/s212275841424-8220https://doaj.org/article/2c45450fa5d14d32b720daf79b9beb0c2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7584https://doaj.org/toc/1424-8220Human Gait Recognition (HGR) is a biometric technique that has been utilized for security purposes for the last decade. The performance of gait recognition can be influenced by various factors such as wearing clothes, carrying a bag, and the walking surfaces. Furthermore, identification from differing views is a significant difficulty in HGR. Many techniques have been introduced in the literature for HGR using conventional and deep learning techniques. However, the traditional methods are not suitable for large datasets. Therefore, a new framework is proposed for human gait recognition using deep learning and best feature selection. The proposed framework includes data augmentation, feature extraction, feature selection, feature fusion, and classification. In the augmentation step, three flip operations were used. In the feature extraction step, two pre-trained models were employed, Inception-ResNet-V2 and NASNet Mobile. Both models were fine-tuned and trained using transfer learning on the CASIA B gait dataset. The features of the selected deep models were optimized using a modified three-step whale optimization algorithm and the best features were chosen. The selected best features were fused using the modified mean absolute deviation extended serial fusion (MDeSF) approach. Then, the final classification was performed using several classification algorithms. The experimental process was conducted on the entire CASIA B dataset and achieved an average accuracy of 89.0. Comparison with existing techniques showed an improvement in accuracy, recall rate, and computational time.Faizan SaleemMuhammad Attique KhanMajed AlhaisoniUsman TariqAmmar ArmghanFayadh AleneziJung-In ChoiSeifedine KadryMDPI AGarticlegait recognitionbiometricdata augmentationdeep learningfeatures optimizationfeatures fusionChemical technologyTP1-1185ENSensors, Vol 21, Iss 7584, p 7584 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	gait recognition biometric data augmentation deep learning features optimization features fusion Chemical technology TP1-1185
spellingShingle	gait recognition biometric data augmentation deep learning features optimization features fusion Chemical technology TP1-1185 Faizan Saleem Muhammad Attique Khan Majed Alhaisoni Usman Tariq Ammar Armghan Fayadh Alenezi Jung-In Choi Seifedine Kadry Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion
description	Human Gait Recognition (HGR) is a biometric technique that has been utilized for security purposes for the last decade. The performance of gait recognition can be influenced by various factors such as wearing clothes, carrying a bag, and the walking surfaces. Furthermore, identification from differing views is a significant difficulty in HGR. Many techniques have been introduced in the literature for HGR using conventional and deep learning techniques. However, the traditional methods are not suitable for large datasets. Therefore, a new framework is proposed for human gait recognition using deep learning and best feature selection. The proposed framework includes data augmentation, feature extraction, feature selection, feature fusion, and classification. In the augmentation step, three flip operations were used. In the feature extraction step, two pre-trained models were employed, Inception-ResNet-V2 and NASNet Mobile. Both models were fine-tuned and trained using transfer learning on the CASIA B gait dataset. The features of the selected deep models were optimized using a modified three-step whale optimization algorithm and the best features were chosen. The selected best features were fused using the modified mean absolute deviation extended serial fusion (MDeSF) approach. Then, the final classification was performed using several classification algorithms. The experimental process was conducted on the entire CASIA B dataset and achieved an average accuracy of 89.0. Comparison with existing techniques showed an improvement in accuracy, recall rate, and computational time.
format	article
author	Faizan Saleem Muhammad Attique Khan Majed Alhaisoni Usman Tariq Ammar Armghan Fayadh Alenezi Jung-In Choi Seifedine Kadry
author_facet	Faizan Saleem Muhammad Attique Khan Majed Alhaisoni Usman Tariq Ammar Armghan Fayadh Alenezi Jung-In Choi Seifedine Kadry
author_sort	Faizan Saleem
title	Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion
title_short	Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion
title_full	Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion
title_fullStr	Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion
title_full_unstemmed	Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion
title_sort	human gait recognition: a single stream optimal deep learning features fusion
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2c45450fa5d14d32b720daf79b9beb0c
work_keys_str_mv	AT faizansaleem humangaitrecognitionasinglestreamoptimaldeeplearningfeaturesfusion AT muhammadattiquekhan humangaitrecognitionasinglestreamoptimaldeeplearningfeaturesfusion AT majedalhaisoni humangaitrecognitionasinglestreamoptimaldeeplearningfeaturesfusion AT usmantariq humangaitrecognitionasinglestreamoptimaldeeplearningfeaturesfusion AT ammararmghan humangaitrecognitionasinglestreamoptimaldeeplearningfeaturesfusion AT fayadhalenezi humangaitrecognitionasinglestreamoptimaldeeplearningfeaturesfusion AT junginchoi humangaitrecognitionasinglestreamoptimaldeeplearningfeaturesfusion AT seifedinekadry humangaitrecognitionasinglestreamoptimaldeeplearningfeaturesfusion
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Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion

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