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RF-Identity: Non-Intrusive Person Identification Based on Commodity RFID Devices

Published: 30 March 2021 Publication History
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  • Abstract

    Person identification plays a critical role in a large range of applications. Recently, RF based person identification becomes a hot research topic due to the contact-free nature of RF sensing that is particularly appealing in current COVID-19 pandemic. However, existing systems still have multiple limitations: i) heavily rely on the gait patterns of users for identification; ii) require a large amount of data to train the model and also extensive retraining for new users and iii) require a large frequency bandwidth which is not available on most commodity RF devices for static person identification. This paper proposes RF-Identity, an RFID-based identification system to address the above limitations and the contribution is threefold. First, by integrating walking pattern features with unique body shape features (e.g., height), RF-Identity achieves a high accuracy in person identification. Second, RF-Identity develops a data augmentation scheme to expand the size of the training data set, thus reducing the human effort in data collection. Third, RF-Identity utilizes the tag diversity in spatial domain to identify static users without a need of large frequency bandwidth. Extensive experiments show an identification accuracy of 94.2% and 95.9% for 50 dynamic and static users, respectively.

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      cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
      Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies  Volume 5, Issue 1
      March 2021
      1272 pages
      EISSN:2474-9567
      DOI:10.1145/3459088
      Issue’s Table of Contents
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      Publication History

      Published: 30 March 2021
      Published in IMWUT Volume 5, Issue 1

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      Author Tags

      1. Deep learning
      2. Person identification
      3. RFID tag
      4. body feature

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      Funding Sources

      • National Natural Science Foundation of China
      • NSFC A3 Foresight Program
      • the Shaanxi International Science and Technology Cooperation Program under grant agreement
      • the ShaanXi Science and Technology Innovation Team Support Project

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      • (2024)Sensing Human Gait for Environment-Independent User Authentication Using Commodity RFID DevicesIEEE Transactions on Mobile Computing10.1109/TMC.2023.331875323:5(6304-6317)Online publication date: May-2024
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