Research on multi-user identity recognition based on Wi-Fi sensing
With the advancement of wireless sensing technology, research on Wi-Fi-based identity recognition has garnered significant attention in fields such as human-computer interaction and home security.While identity recognition based on Wi-Fi signals has achieved initial success, it is currently primaril...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2024-03-01
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| Series: | 物联网学报 |
| Subjects: | |
| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00381/ |
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| _version_ | 1841531304589393920 |
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| author | Zhongcheng WEI Wei CHEN Yanhu DONG Bin LIAN Wei WANG Jijun ZHAO |
| author_facet | Zhongcheng WEI Wei CHEN Yanhu DONG Bin LIAN Wei WANG Jijun ZHAO |
| author_sort | Zhongcheng WEI |
| collection | DOAJ |
| description | With the advancement of wireless sensing technology, research on Wi-Fi-based identity recognition has garnered significant attention in fields such as human-computer interaction and home security.While identity recognition based on Wi-Fi signals has achieved initial success, it is currently primarily suitable for scenarios involving individual user behavior.Identity recognition for multiple users in concurrent behavior scenarios still faces a series of challenges, including issues related to mutual interference between users and poor model robustness.Therefore, a Wiblack system for recognizing multiple user identities in a concurrent distribution behavior scenario was proposed.The core idea was to train a multi-branch deep neural network (Wiblack-Net) to extract unique features for each individual user.Firstly, the common features among multiple users were extracted using the backbone network.Then, a binary classifier was assigned to each user to determine the presence of the target user within a given group, thereby achieving identity recognition for multiple users based on concurrent behavior.In addition, experiments comparing Wiblack with several independent binary classification models and a single multiclassification model were conducted to analyze operational efficiency.System performance experimental results demonstrate that when simultaneously identifying the identities of three users, Wibalck achieves an average accuracy of 92.97%, an average precision of 93.71%, an average recall of 93.24%, and an average F1 score of 92.43%. |
| format | Article |
| id | doaj-art-1a73c546a1254b40a5c2ef70539cd0e5 |
| institution | Kabale University |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2024-03-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 物联网学报 |
| spelling | doaj-art-1a73c546a1254b40a5c2ef70539cd0e52025-01-15T02:51:39ZzhoChina InfoCom Media Group物联网学报2096-37502024-03-01811112155296508Research on multi-user identity recognition based on Wi-Fi sensingZhongcheng WEIWei CHENYanhu DONGBin LIANWei WANGJijun ZHAOWith the advancement of wireless sensing technology, research on Wi-Fi-based identity recognition has garnered significant attention in fields such as human-computer interaction and home security.While identity recognition based on Wi-Fi signals has achieved initial success, it is currently primarily suitable for scenarios involving individual user behavior.Identity recognition for multiple users in concurrent behavior scenarios still faces a series of challenges, including issues related to mutual interference between users and poor model robustness.Therefore, a Wiblack system for recognizing multiple user identities in a concurrent distribution behavior scenario was proposed.The core idea was to train a multi-branch deep neural network (Wiblack-Net) to extract unique features for each individual user.Firstly, the common features among multiple users were extracted using the backbone network.Then, a binary classifier was assigned to each user to determine the presence of the target user within a given group, thereby achieving identity recognition for multiple users based on concurrent behavior.In addition, experiments comparing Wiblack with several independent binary classification models and a single multiclassification model were conducted to analyze operational efficiency.System performance experimental results demonstrate that when simultaneously identifying the identities of three users, Wibalck achieves an average accuracy of 92.97%, an average precision of 93.71%, an average recall of 93.24%, and an average F1 score of 92.43%.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00381/Wi-Fi sensingchannel state informationidentity recognitionmulti-user recognitionmulti-branch deep neural network |
| spellingShingle | Zhongcheng WEI Wei CHEN Yanhu DONG Bin LIAN Wei WANG Jijun ZHAO Research on multi-user identity recognition based on Wi-Fi sensing 物联网学报 Wi-Fi sensing channel state information identity recognition multi-user recognition multi-branch deep neural network |
| title | Research on multi-user identity recognition based on Wi-Fi sensing |
| title_full | Research on multi-user identity recognition based on Wi-Fi sensing |
| title_fullStr | Research on multi-user identity recognition based on Wi-Fi sensing |
| title_full_unstemmed | Research on multi-user identity recognition based on Wi-Fi sensing |
| title_short | Research on multi-user identity recognition based on Wi-Fi sensing |
| title_sort | research on multi user identity recognition based on wi fi sensing |
| topic | Wi-Fi sensing channel state information identity recognition multi-user recognition multi-branch deep neural network |
| url | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00381/ |
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