A One-Class Classification Method for Human Gait Authentication Using Micro-Doppler Signatures

Journal article (2021)

Authors

Haoran Ji Tianjin University
Chunping Hou Tianjin University
Yang Yang Tianjin University
F. Fioranelli Microwave Sensing, Signals & Systems - EEMCS
Yue Lang Hebei University of Technology
Research Group
Microwave Sensing, Signals & Systems (EEMCS) (TU Delft)
Copyright: © 2021 Haoran Ji, Chunping Hou, Yang Yang, F. Fioranelli, Yue Lang
DOI
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https://doi.org/10.1109/LSP.2021.3122344
Gait authentication Micro-Doppler radar One class classification
More Info
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Published Date

2021

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Microwave Sensing, Signals & Systems

Abstract

In this letter, a radar-based gait authentication method is proposed. We focus on the overfitting problem on the target category caused by limited training data in authentication models and propose a one-class classification model to alleviate this problem. The effectiveness of such model is verified by establishing a radar-based gait dataset, which is composed of gait micro-Doppler spectrograms derived from nine human subjects. The experimental results demonstrate that, under the condition of limited training data, the performances of an authentication model degrade because misclassification of the non-target samples easily occurs. The proposed method effectively avoids this risk, performing the other existing authentication and one-class classification methods on the metric Equal Error Rate.