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

Haoran Ji, Chunping Hou, Yang Yang, Francesco Fioranelli, Yue Lang

Research output: Contribution to journalArticleScientificpeer-review

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.
Original languageEnglish
Article number9585408
Pages (from-to)2182-2186
Number of pages5
JournalIEEE Signal Processing Letters
Volume28
DOIs
Publication statusPublished - 2021

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care

Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Gait authentication
  • micro-Doppler radar
  • one class classification

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