| Autor: |
Zhongcheng WEI, Wei CHEN, Yanhu DONG, Bin LIAN, Wei WANG, Jijun ZHAO |
| Jazyk: |
čínština |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
物联网学报, Vol 8, Pp 111-121 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2096-3750 |
| DOI: |
10.11959/j.issn.2096-3750.2024.00381 |
| Popis: |
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%. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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