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|a 10.1007/s11227-023-05156-9
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|a eng
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|a Yousef, Reem N
|e verfasserin
|4 aut
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| 245 |
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|a Model-based and model-free deep features fusion for high performed human gait recognition
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|c 2023
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 14.09.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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| 520 |
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|a © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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|a In the last decade, the need for a non-contact biometric model for recognizing candidates has increased, especially after the pandemic of COVID-19 appeared and spread worldwide. This paper presents a novel deep convolutional neural network (CNN) model that guarantees quick, safe, and precise human authentication via their poses and walking style. The concatenated fusion between the proposed CNN and a fully connected model has been formulated, utilized, and tested. The proposed CNN extracts the human features from two main sources: (1) human silhouette images according to model-free and (2) human joints, limbs, and static joint distances according to a model-based via a novel, fully connected deep-layer structure. The most commonly used dataset, CASIA gait families, has been utilized and tested. Numerous performance metrics have been evaluated to measure the system quality, including accuracy, specificity, sensitivity, false negative rate, and training time. Experimental results reveal that the proposed model can enhance recognition performance in a superior manner compared with the latest state-of-the-art studies. Moreover, the suggested system introduces a robust real-time authentication with any covariate conditions, scoring 99.8% and 99.6% accuracy in identifying casia (B) and casia (A) datasets, respectively
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|a Journal Article
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|a CASIA gait dataset
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| 650 |
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|a Convolutional neural network (CNN)
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| 650 |
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|a Model-based method
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|a Model-free method
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| 650 |
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|a Silhouette images
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| 700 |
1 |
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|a Khalil, Abeer T
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Samra, Ahmed S
|e verfasserin
|4 aut
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| 700 |
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|a Ata, Mohamed Maher
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t The Journal of supercomputing
|d 1998
|g (2023) vom: 19. März, Seite 1-38
|w (DE-627)NLM098252410
|x 0920-8542
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|g year:2023
|g day:19
|g month:03
|g pages:1-38
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|u http://dx.doi.org/10.1007/s11227-023-05156-9
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