Accelerometer based Gait Recognition using Adapted Gaussian Mixture Models

Muhammad Muaaz, Rene Mayrhofer

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

9 Citations (Scopus)

Abstract

Gait authentication using a cell phone based accelerometer sensor offers an unobtrusive, user-friendly, and a periodic way of authenticating individuals to their smartphones. In this paper, we present a GMM-UBM based gait recognition approach for a realistic scenario (when the phone is placed inside the trouser pocket and the user is walking) by using the magnitude data of a smartphone-based tri-axes accelerometer sensor. To evaluate our approach we use a gait data set of 35 participants collected at their respective normal walking pace in two different sessions with an average gap of 25 days between the sessions. We obtained EERs of 3.031%, 11.531%, and 14.393% for the same-day, mix-days, and cross-days, respectively.

Original languageEnglish
Title of host publication14th International Conference on Advances in Mobile Computing and Multimedia, MoMM 2016 - Proceedings
EditorsBessam Abdulrazak, Matthias Steinbauer, Ismail Khalil, Eric Pardede, Gabriele Anderst-Kotsis
PublisherACM Press
Pages288-291
Number of pages4
ISBN (Electronic)9781450348065
ISBN (Print)978-1-4503-4806-5
DOIs
Publication statusPublished - 28 Nov 2016
Event14th International Conference on Advances in Mobile Computing and Multimedia (MoMM 2016) - Singapore, Singapore
Duration: 28 Nov 201630 Nov 2016
http://www.iiwas.org/conferences/momm2016/

Publication series

NameACM International Conference Proceeding Series

Conference

Conference14th International Conference on Advances in Mobile Computing and Multimedia (MoMM 2016)
CountrySingapore
CitySingapore
Period28.11.201630.11.2016
Internet address

Keywords

  • Accelerometer
  • gait recognition
  • Gaussian Mixture Models
  • segmentation
  • variance
  • Segmentation
  • Gaussian mixture models
  • Gait recognition
  • Variance

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