Multimodal Adaptive Identity-Recognition Algorithm Fused with Gait Perception
Identity-recognition technologies require assistive equipment, whereas they are poor in recognition accuracy and expensive. To overcome this deficiency, this paper proposes several gait feature identification algorithms. First, in combination with the collected gait information of individuals from t...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2021-12-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2021.9020006 |
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| author | Changjie Wang Zhihua Li Benjamin Sarpong |
| author_facet | Changjie Wang Zhihua Li Benjamin Sarpong |
| author_sort | Changjie Wang |
| collection | DOAJ |
| description | Identity-recognition technologies require assistive equipment, whereas they are poor in recognition accuracy and expensive. To overcome this deficiency, this paper proposes several gait feature identification algorithms. First, in combination with the collected gait information of individuals from triaxial accelerometers on smartphones, the collected information is preprocessed, and multimodal fusion is used with the existing standard datasets to yield a multimodal synthetic dataset; then, with the multimodal characteristics of the collected biological gait information, a Convolutional Neural Network based Gait Recognition (CNN-GR) model and the related scheme for the multimodal features are developed; at last, regarding the proposed CNN-GR model and scheme, a unimodal gait feature identity single-gait feature identification algorithm and a multimodal gait feature fusion identity multimodal gait information algorithm are proposed. Experimental results show that the proposed algorithms perform well in recognition accuracy, the confusion matrix, and the kappa statistic, and they have better recognition scores and robustness than the compared algorithms; thus, the proposed algorithm has prominent promise in practice. |
| format | Article |
| id | doaj-art-a260b4768f8844b3941f3d3f28653e75 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-a260b4768f8844b3941f3d3f28653e752025-02-02T23:47:26ZengTsinghua University PressBig Data Mining and Analytics2096-06542021-12-014422323210.26599/BDMA.2021.9020006Multimodal Adaptive Identity-Recognition Algorithm Fused with Gait PerceptionChangjie Wang0Zhihua Li1Benjamin Sarpong2<institution content-type="dept">Department of Computer Science and Technology, School of Artificial Intelligence and Computer Science</institution>, <institution>Jiangnan University</institution>, <city>Wuxi</city> <postal-code>214122</postal-code>, <country>China</country><institution content-type="dept">Department of Computer Science and Technology, School of Artificial Intelligence and Computer Science</institution>, <institution>Jiangnan University</institution>, <city>Wuxi</city> <postal-code>214122</postal-code>, <country>China</country><institution content-type="dept">Department of Computer Science and Technology, School of Artificial Intelligence and Computer Science</institution>, <institution>Jiangnan University</institution>, <city>Wuxi</city> <postal-code>214122</postal-code>, <country>China</country>Identity-recognition technologies require assistive equipment, whereas they are poor in recognition accuracy and expensive. To overcome this deficiency, this paper proposes several gait feature identification algorithms. First, in combination with the collected gait information of individuals from triaxial accelerometers on smartphones, the collected information is preprocessed, and multimodal fusion is used with the existing standard datasets to yield a multimodal synthetic dataset; then, with the multimodal characteristics of the collected biological gait information, a Convolutional Neural Network based Gait Recognition (CNN-GR) model and the related scheme for the multimodal features are developed; at last, regarding the proposed CNN-GR model and scheme, a unimodal gait feature identity single-gait feature identification algorithm and a multimodal gait feature fusion identity multimodal gait information algorithm are proposed. Experimental results show that the proposed algorithms perform well in recognition accuracy, the confusion matrix, and the kappa statistic, and they have better recognition scores and robustness than the compared algorithms; thus, the proposed algorithm has prominent promise in practice.https://www.sciopen.com/article/10.26599/BDMA.2021.9020006gait recognitionperson identificationdeep learningmultimodal feature fusion |
| spellingShingle | Changjie Wang Zhihua Li Benjamin Sarpong Multimodal Adaptive Identity-Recognition Algorithm Fused with Gait Perception Big Data Mining and Analytics gait recognition person identification deep learning multimodal feature fusion |
| title | Multimodal Adaptive Identity-Recognition Algorithm Fused with Gait Perception |
| title_full | Multimodal Adaptive Identity-Recognition Algorithm Fused with Gait Perception |
| title_fullStr | Multimodal Adaptive Identity-Recognition Algorithm Fused with Gait Perception |
| title_full_unstemmed | Multimodal Adaptive Identity-Recognition Algorithm Fused with Gait Perception |
| title_short | Multimodal Adaptive Identity-Recognition Algorithm Fused with Gait Perception |
| title_sort | multimodal adaptive identity recognition algorithm fused with gait perception |
| topic | gait recognition person identification deep learning multimodal feature fusion |
| url | https://www.sciopen.com/article/10.26599/BDMA.2021.9020006 |
| work_keys_str_mv | AT changjiewang multimodaladaptiveidentityrecognitionalgorithmfusedwithgaitperception AT zhihuali multimodaladaptiveidentityrecognitionalgorithmfusedwithgaitperception AT benjaminsarpong multimodaladaptiveidentityrecognitionalgorithmfusedwithgaitperception |