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Cited by 305Gait Recognition and AnalysisUser Authentication and Security SystemsBiometric Identification and Security

Deep Learning-Based Gait Recognition Using Smartphones in the Wild

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Zou, Qin, Wang, Yanling, Wang, Qian, Zhao, Yi, Li, Qingquan·2020·IEEE Transactions on Information Forensics and Security
DOI 10.1109/tifs.2020.2985628
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Abstract

Compared to other biometrics, gait is difficult to conceal and has the advantage of being unobtrusive. Inertial sensors, such as accelerometers and gyroscopes, are often used to capture gait dynamics. These inertial sensors are commonly integrated into smartphones and are widely used by the average person, which makes gait data convenient and inexpensive to collect. In this paper, we study gait recognition using smartphones in the wild. In contrast to traditional methods, which often require a person to walk along a specified road and/or at a normal walking speed, the proposed method collects inertial gait data under unconstrained conditions without knowing when, where, and how the user walks. To obtain good person identification and authentication performance, deep-learning techniques are presented to learn and model the gait biometrics based on walking data. Specifically, a hybrid deep neural network is proposed for robust gait feature representation, where features in the space and time domains are successively abstracted by a convolutional neural network and a recurrent neural network. In the experiments, two datasets collected by smartphones for a total of 118 subjects are used for evaluations. The experiments show that the proposed method achieves higher than 93.5% and 93.7% accuracies in person identification and authentication, respectively.

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  • Our chosen model achieved the best testing result of 99.93% for H-Activity dataset and 98.76% and 93.11% for MHEALTH and UCI-HAR dataset respectively.

    “In contrast to these three models, i.e.,sequential LSTM layers (LSTM-CNN) before Convolution layer [48], sequential Convolution and dropout layers (CNN-LSTM) before LSTM layer [59], parallel LSTM layers (Parrallel LSTM-CNN) with Convolution layer [60], our chosen model achieved the best testing result of 99.93% for H-Activity dataset and 98.76% and 93.11% for MHEALTH and UCI-HAR dataset respective”

    From Deep CNN-LSTM With Self-Attention Model for Human Activity Recognition Using Wearable Sensor (2022) · cites this paper as [60]

  • All of the above-mentioned models [48], [59], [60] were implemented with exact parameters described in the respective literature except the input and output layer to match our dataset.

    “Hence, all of the above-mentioned models [48], [59], [60] were implemented with exact parameters described in the respective literature except the input and output layer to match our dataset.”

    From Deep CNN-LSTM With Self-Attention Model for Human Activity Recognition Using Wearable Sensor (2022) · cites this paper as [60]

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Related

Authors
Zou, Q., Wang, Y., Wang, Q., Zhao, Y., & Li, Q.
Affiliations
  • Wuhan University (CN)
  • Shenzhen University (CN)
ORCID
Venue
IEEE Transactions on Information Forensics and Security
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2020
Volume
15
Pages
3197-3212
ISSN
  • 1556-6013 (ISSN-L)
  • 1556-6021
DOI
10.1109/tifs.2020.2985628
OpenAlex
W3014370958
Cited by
305
References
95
Citations by year
2026: 20 · 2025: 38 · 2024: 58 · 2023: 52 · 2022: 62 · 2021: 44 · 2020: 27 · 2019: 4
FWCI
14.53
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Information Forensics and Security (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • NSFC (61872277, 61822207, U1636219, U1934215, 91546106)
  • Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (16S01)
  • Hubei Provincial Natural Science Foundation (2018CFB482)
  • Equipment Pre-Research Joint Fund of the Ministry of Education of China (6141A02033327)
  • Outstanding Youth Foundation of Hubei Province (2017CFA047)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
  • Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
  • Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
Keywords
gait recognition, smartphone inertial sensors, personal identification, user authentication, hybrid deep neural network, convolutional neural networks, recurrent neural networks, gait biometrics

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