research-article

Au-Id: Automatic User Identification and Authentication through the Motions Captured from Sequential Human Activities Using RFID

Authors:

Qian ZhangAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 3, Issue 2

Article No.: 48, Pages 1 - 26

https://doi.org/10.1145/3328919

Published: 21 June 2019 Publication History

Abstract

The advancements of ambient intelligence and ubiquitous computing are driving the unprecedented development of smart spaces where enhanced services are provided based on activity recognition. Meanwhile, user identification, which can enable the personalization of the enhanced services for specific users and the access control of confidential information, becomes increasingly important. Traditional approaches to user identification require either attached wearable sensors or active user participation. This paper presents Au-Id, a non-intrusive automatic user identification and authentication system through human motions captured from their daily activities based on RFID. The key insight is that the RFID tag array can capture human's physical and behavioral characteristics for user identification. Particularly, phase and RSSI data streams of the RFID tag array are fused to incorporate the information from time, space and modality dimensions. Based on this, a novel sequence labeling based segmentation method is proposed for target motion extraction. Then Au-Id leverages a multi-modal Convolutional Neural Network (CNN) for user identification and significantly reduces the training efforts by transfer learning. In addition, Au-Id facilitates user authentication by integrating the feature representations extracted by CNN with one-class SVM classifiers. The evaluation shows that Au-Id can achieve accurate and robust user identification and authentication.

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Cited By

Zheng WZhang YJiang LZhang DGu T(2024)MeshID: Few-Shot Finger Gesture Based User Identification Using Orthogonal Signal InterferenceSensors10.3390/s2406197824:6(1978)Online publication date: 20-Mar-2024
https://doi.org/10.3390/s24061978
Nie MZou LCui HZhou XWan Y(2024)Enhancing Human Activity Recognition with LoRa Wireless RF Signal Preprocessing and Deep LearningElectronics10.3390/electronics1302026413:2(264)Online publication date: 6-Jan-2024
https://doi.org/10.3390/electronics13020264
Chen YYu JKong LZhu YTang F(2024)Sensing Human Gait for Environment-Independent user Authentication using Commodity RFID DevicesIEEE Transactions on Mobile Computing10.1109/TMC.2023.3318753(1-13)Online publication date: 2024
https://doi.org/10.1109/TMC.2023.3318753
Show More Cited By

Index Terms

Au-Id: Automatic User Identification and Authentication through the Motions Captured from Sequential Human Activities Using RFID
1. Human-centered computing
  1. Ubiquitous and mobile computing
2. Security and privacy
  1. Security services
    1. Authentication

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Published In

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 3, Issue 2

June 2019

802 pages

EISSN:2474-9567

DOI:10.1145/3341982

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Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 21 June 2019

Accepted: 01 April 2019

Revised: 01 April 2019

Received: 01 February 2019

Published in IMWUT Volume 3, Issue 2

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Cited By

Zheng WZhang YJiang LZhang DGu T(2024)MeshID: Few-Shot Finger Gesture Based User Identification Using Orthogonal Signal InterferenceSensors10.3390/s2406197824:6(1978)Online publication date: 20-Mar-2024
https://doi.org/10.3390/s24061978
Nie MZou LCui HZhou XWan Y(2024)Enhancing Human Activity Recognition with LoRa Wireless RF Signal Preprocessing and Deep LearningElectronics10.3390/electronics1302026413:2(264)Online publication date: 6-Jan-2024
https://doi.org/10.3390/electronics13020264
Chen YYu JKong LZhu YTang F(2024)Sensing Human Gait for Environment-Independent user Authentication using Commodity RFID DevicesIEEE Transactions on Mobile Computing10.1109/TMC.2023.3318753(1-13)Online publication date: 2024
https://doi.org/10.1109/TMC.2023.3318753
Xu WLiu JZhang SZheng YLin FXiao FHan J(2024)Anti-Spoofing Facial Authentication Based on COTS RFIDIEEE Transactions on Mobile Computing10.1109/TMC.2023.3289708(1-17)Online publication date: 2024
https://doi.org/10.1109/TMC.2023.3289708
Guo QXia MYan DWang JShi CWang QLi T(2024)Motion Pattern Recognition for Indoor Pedestrian Altitude Estimation Based on Inertial SensorIEEE Sensors Journal10.1109/JSEN.2024.335516324:6(8197-8209)Online publication date: 15-Mar-2024
https://doi.org/10.1109/JSEN.2024.3355163
Yang JYu JKong LZhu YDai H(2024)OpenAuth: Human Body-Based User Authentication Using mmWave Signals in Open-World Scenarios2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS60910.2024.00125(1330-1341)Online publication date: 23-Jul-2024
https://doi.org/10.1109/ICDCS60910.2024.00125
Xiao YChen YNie MZhu TLiu ZLiu C(2023)Exploring LoRa and Deep Learning-Based Wireless Activity RecognitionElectronics10.3390/electronics1203062912:3(629)Online publication date: 27-Jan-2023
https://doi.org/10.3390/electronics12030629
Laporte MGjoreski MLangheinrich M(2023)LAUREATEProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36108927:3(1-41)Online publication date: 27-Sep-2023
https://dl.acm.org/doi/10.1145/3610892
Luo ZYan MWang WZhang Q(2023)Non-intrusive Anomaly Detection of Industrial Robot Operations by Exploiting Nonlinear EffectProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35694776:4(1-27)Online publication date: 11-Jan-2023
https://doi.org/10.1145/3569477
Huh JShin HKim HCheon ESong YLee COakley I(2023)WristAcousticProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35694736:4(1-34)Online publication date: 11-Jan-2023
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