Computer Science ›› 2020, Vol. 47 ›› Issue (11): 25-31.doi: 10.11896/jsjkx.200200044

Special Issue: Intelligent Mobile Authentication

• Intelligent Mobile Authentication • Previous Articles     Next Articles

LWID:Lightweight Gait Recognition Model Based on WiFi Signals

ZHOU Zhi-yi1, SHONG Bing2, DUAN Peng-song1, CAO Yang-jie1   

  1. 1 School of Software Engineering,Zhengzhou University,Zhengzhou 450000,China
    2 Henan Police College network Security Department,Zhengzhou 450046,China
  • Received:2020-02-08 Revised:2020-07-03 Online:2020-11-15 Published:2020-11-05
  • About author:ZHOU Zhi-yi,born in 1993,postgra-duate.His main research interests include application of deep learning in the field of WiFi.
    CAO Yang-jie,born in 1976,Ph.D.His current research interests include computer vision and intelligent computing,artificial intelligence and high-perfor-mance computing.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China(61972092).

Abstract: As an important research of pervasive computing and human-computer interaction,identity recognition is widely researched.Although traditional WiFi based identification methods have made good progress,they still face challenges such as limi-ted classification ability,high storage cost and long training time.The above problems motivate us to propose a lightweight gait recognition model based on multi-layer neural networks,which is named as LWID(LightWeight Identification).We firstly reconstruct original time series data into graphs to retain characteristic information among different carriers to the maximum extent.Then we design a bionic Balloon mechanism to tailor neurons in network layer.By combining convolution kernels of different size,we extract data features and integrate channel information in the feature map.The proposed method realizes model scale lightweight with higher classification ability.Experimental results show that the model has 98.8% recognition rate in a 50-person dataset.Compared with traditional WiFi based identification model,LWID has stronger classification ability and robustness.Meanwhile,the model is compressed to 6.14% of current computer vision model size with same accuracy.

Key words: Balloon mechanism, Frequency energy diagram, Gait recognition, Light weight identification, Model compression

CLC Number: 

  • TP391
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