Deep Learning-based identification of human gait by radar micro-Doppler measurements

V. S. Papanastasiou, R. P. Trommel, R. I.A. Harmanny, A. Yarovoy

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

For the first time identification of human individuals using micro-Doppler (m-D) features measured at X-band has been demonstrated. Deep Convolutional Neural Networks (DCNNs) have been used to perform classification. Inspection and visualization of the classification results were performed using Uniform Manifold Approximation and Projection (UMAP). Classification accuracy of above 93.5% is obtained for a population of 22 subjects. The results show that human identification on a specific population based on X-band m-D measurements can be performed reliably using a DCNN.

Original languageEnglish
Title of host publicationEuRAD 2020 - 2020 17th European Radar Conference
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages49-52
Number of pages4
ISBN (Electronic)9782874870613
DOIs
Publication statusPublished - 2021
Event17th European Radar Conference, EuRAD 2020 - Utrecht, Netherlands
Duration: 13 Jan 202115 Jan 2021

Publication series

NameEuRAD 2020 - 2020 17th European Radar Conference

Conference

Conference17th European Radar Conference, EuRAD 2020
CountryNetherlands
CityUtrecht
Period13/01/2115/01/21

Keywords

  • classification
  • deep learning
  • identification
  • micro-Doppler
  • radar

Fingerprint Dive into the research topics of 'Deep Learning-based identification of human gait by radar micro-Doppler measurements'. Together they form a unique fingerprint.

Cite this