With the advancement of wireless sensing technology,research on Wi-Fi-based identity recognition has gar-nered 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,in-cluding 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 classifi-cation models and a single multiclassification model were conducted to analyze operational efficiency.System perfor-mance 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
Wi-Fi感知/信道状态信息/身份识别/多人识别/多分支深度神经网络
Key words
Wi-Fi sensing/channel state information/identity recognition/multi-user recognition/multi-branch deep neural network
维普
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