Research on multi-user identity recognition based on Wi-Fi sensing

Zhongcheng WEI; Wei CHEN; Yanhu DONG; Bin LIAN; Wei WANG; Jijun ZHAO

doi:10.11959/j.issn.2096-3750.2024.00381

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Research on multi-user identity recognition based on Wi-Fi sensing

Theory and Technology | 更新时间：2024-06-05

- Research on multi-user identity recognition based on Wi-Fi sensing
- Chinese Journal on Internet of Things Vol. 8, Issue 1, Pages: 111-121(2024)
- 作者机构：
  
  1. 河北工程大学信息与电气工程学院，河北邯郸 056038
  2. 河北省安防信息感知与处理重点实验室，河北邯郸 056038
  3. 河北工程大学水利水电学院，河北邯郸 056038
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2024.00381
  CLC： TN92
- Revised：2023-12-08，
  
  Online First：2024-03，
  
  Published：30 March 2024
- 稿件说明：
移动端阅览
Zhongcheng WEI, Wei CHEN, Yanhu DONG, et al. Research on multi-user identity recognition based on Wi-Fi sensing[J]. Chinese Journal on Internet of Things, 2024, 8(1): 111-121.
DOI：

Zhongcheng WEI, Wei CHEN, Yanhu DONG, et al. Research on multi-user identity recognition based on Wi-Fi sensing[J]. Chinese Journal on Internet of Things, 2024, 8(1): 111-121. DOI： 10.11959/j.issn.2096-3750.2024.00381.

摘要

随着无线感知技术的发展，基于Wi-Fi的身份识别研究在人机交互和家居安防等领域备受关注。尽管基于Wi-Fi信号的身份识别已经取得了初步的成功，但是目前主要适用于用户独立行为场景，并发行为下的多用户身份识别仍然面临着一系列挑战，包括用户之间的相互干扰以及模型鲁棒性差等问题。因此，提出了一种并发行为下多用户身份识别系统Wiblack，其核心思想是训练一个多分支深度神经网络（Wiblack-Net）来提取每个单用户的独特特征。首先，利用主干网络提取多用户之间的共同特征；然后，为每个用户分配一个二分类器以此判断给定群体中是否存在目标用户，在此基础上基于并发行为实现多个用户身份识别。此外，将Wiblack与多个独立的二分类模型和单个多分类模型进行对比实验，对运行效率和系统性能进行分析。实验结果显示，在同时识别3个用户身份时，Wibalck平均准确率达到了92.97%，平均精确度为93.71%，平均召回率为93.24%，平均F1值为92.43%。

Abstract

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%.

关键词

Keywords

references

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