A multi-view human gait recognition using hybrid whale and gray wolf optimization algorithm with a random forest classifier
Gait recognition has become one of the furthest promising behavioral biometric techniques for identifying individuals. Gait recognition models are capable of identifying humans at a distance based on their walking manner without their permission or interference. However, it is usually noted that the...
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| Vydáno v: | Image and vision computing Ročník 136; s. 104721 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.08.2023
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| Témata: | |
| ISSN: | 0262-8856, 1872-8138 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Gait recognition has become one of the furthest promising behavioral biometric techniques for identifying individuals. Gait recognition models are capable of identifying humans at a distance based on their walking manner without their permission or interference. However, it is usually noted that the performance of a gait recognition approach will drop drastically in the presence of covariates such as carrying conditions, clothing conditions, and variations in the view angle. Therefore, it is necessary to develop a robust gait recognition system in order to identify the most significant gait features. In this paper, we introduced a recently developed hybrid whale and gray wolf optimization algorithm (WGWOA) for determining the optimal subset of gait features by combining the advantages of whale optimization and gray wolf optimization techniques. Moreover, we employed principal component analysis (PCA) to extract the essential gait features from the gradient gait energy image (GGEI) and random forest (RF) approach to classify the optimal gait features. The proposed method has been assessed on the publicly available largest multi-view CASIA-B and OU-MVLP benchmark datasets. Experimental results indicate that the proposed model achieved an accuracy of 99.25%, 98.39%, and 97.97% under normal, carrying a bag and wearing coat walking conditions, respectively on the CASIA-B dataset. Furthermore, the proposed model achieved an accuracy of 97.63% under normal conditions on the OU-MVLP dataset. The comparative results also confirm that the proposed algorithm is superior to the contemporary approaches.
•An improved Gradient Gait Energy Image (GGEI) spatiotemporal template is used.•PCA is used to extract additional gait features for enhancing pattern classification.•To make it robust, a novel hybrid whale and gray wolf optimization algorithm is used.•The technique is integrated with Levy flight strategy for improving search.•The selected gait features are classified by using the random forest algorithm. |
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| ISSN: | 0262-8856 1872-8138 |
| DOI: | 10.1016/j.imavis.2023.104721 |