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WiGId: Indoor Group Identification with CSI-Based Random Forest.

Xiaochao Dang1,2, Yuan Cao1, Zhanjun Hao1,2

  • 1College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China.

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Summary
This summary is machine-generated.

This study introduces WiGId, a novel Wi-Fi-based human identity recognition system. It effectively identifies small groups without extra sensors, overcoming limitations of traditional biometrics.

Keywords:
channel state informationhuman identificationrandom forestwireless sensing

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Area of Science:

  • Computer Science
  • Signal Processing
  • Biometrics

Background:

  • Traditional biometric identification methods often require specialized hardware, limiting their applicability in certain scenarios.
  • Existing Wi-Fi-based human recognition systems face challenges in accurately identifying small groups (2-5 individuals) simultaneously.
  • Gait analysis and fingerprint classification present complexities in storage and processing for group identification.

Purpose of the Study:

  • To develop a Wi-Fi-based human identity recognition system capable of identifying small groups without additional sensors.
  • To address the limitations of existing methods in handling multi-person identification and complex data processing.
  • To leverage Channel State Information (CSI) for robust and accessible identity recognition.

Main Methods:

  • A two-stage approach involving offline training and online real-time classification using a random forest classifier.
  • Extraction of Channel State Information (CSI) features, treating multiple individuals as a single entity to simplify feature selection.
  • Utilizing a random forest classifier for its ability to handle high-dimensional data and generalize well.

Main Results:

  • The proposed WiGId system demonstrated effective human identity recognition using Wi-Fi signals.
  • The method achieved good recognition performance in both Line of Sight (LOS) and Non-Line of Sight (NLOS) environments.
  • The random forest classifier proved adept at classifying CSI features for group identification.

Conclusions:

  • Wi-Fi-based identity recognition offers a promising, equipment-free alternative to traditional biometrics.
  • The WiGId system provides a viable solution for identifying small groups in diverse environmental conditions.
  • The random forest classifier is a suitable tool for processing complex CSI data in identity recognition tasks.