NDLI: Human identification based on natural gait micro-Doppler signatures using deep transfer learning

Content Provider	IET Digital Library
Author	Ni, Zhongfei Huang, Binke
Abstract	Gait-based human identification aims to identify individuals by their walking style. In this study, the authors investigate the use of micro-Doppler (m-D) signatures retrieved from a frequency-modulated continuous-wave radar sensor to identify individuals based on their natural gait characteristics. The gait dataset of 20 persons has been collected in an indoor environment where each subject was allowed to walk naturally and freely, which is absolutely more realistic and challenging than most existing works based on limited walking behaviour. Then, they perform identification using a transfer learned ResNet-50, which was fine-tuned on the gait m-D dataset based on the deep transfer learning technique. Through experiments, they first determined the optimal observation window length of m-D samples, and with this input, they achieved an average identification accuracy of 96.7% on the test set for 20 subjects, which highly outperforms the state-of-the-art methods. The presented work provides prospects in developing a solution to automatically identify persons based on gait characteristics using a simple and cost-efficient radar device.
Starting Page	1640
Ending Page	1646
Page Count	7
ISSN	17518784
Volume Number	14
e-ISSN	17518792
Issue Number	Issue 10, Oct (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/14/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2020.0183
Journal	IET Radar, Sonar & Navigation
Publisher Date	2020-07-30
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Average Identification Accuracy CW Radar Deep Transfer Learning Technique Digital Signal Processing Doppler Radar Electrical Engineering Computing Feature Extraction Frequency-modulated Continuous-wave Radar Sensor Gait Analysis Gait Dataset Gait M-D Gait-based Human Identification Indoor Environment Knowledge Engineering Technique Learning in AI Natural Gait Characteristic Natural Gait MicroDoppler Signatures Optimal Observation Window Length Radar Computing Radar Equipment Radar Signal Processing Signal Processing And Detection System And Application Walking Behaviour Walking Style
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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