A Deep Learning Algorithm for Contactless Human Identification

被引：5

作者：

Yu X. ^{[1
]}

Chen W. ^{[1
]}

Wang D. ^{[1
]}

Cao Y. ^{[1
]}

Chen H. ^{[2
]}

机构：

[1] School of Software Engineering, Zhengzhou University, Zhengzhou

[2] School of Electronic and Information Engineering, Foshan University, Foshan, 528000, Guangdong

来源：

Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University | 2019年 / 53卷 / 04期

关键词：

Channel state information; Contactless human identification; Deep learning;

D O I：

10.7652/xjtuxb201904018

中图分类号：

TN911 [通信理论];

学科分类号：

081002 ;

摘要：

A contactless human identification algorithm WiID based on deep learning is proposed to solve the problem that traditional Wi-Fi-based human identification methods have low efficiency of manual feature extraction and low accuracy. An input matrix for deep learning is established by analyzing the spatial correlation of channel state information data in subcarriers. The two-dimensional convolution operation is used to extract local spatial features from adjacent subcarriers, and spatial features are modeled from the temporal dimension through the gated recurrent unit layer. Gait feature extractions in both spatial and temporal dimensions are performed, and the end-to-end contactless human identification is realized to effectively reduce the workload of data preprocessing. Experimental results and comparisons with the convolutional neural network and recurrent neural network show that the identification accuracy of the proposed algorithm is effectively improved. The identification accuracy of the proposed algorithm under six different scenarios ranges from 92.9% to 95.6%, and the algorithm has good identification effect and robustness. © 2019, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.

引用

页码：122 / 127

页数：5

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