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 language | English |
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Title of host publication | 14th International Conference on Advances in Mobile Computing and Multimedia, MoMM 2016 - Proceedings |
Editors | Bessam Abdulrazak, Matthias Steinbauer, Ismail Khalil, Eric Pardede, Gabriele Anderst-Kotsis |
Publisher | ACM Press |
Pages | 288-291 |
Number of pages | 4 |
ISBN (Electronic) | 9781450348065 |
ISBN (Print) | 978-1-4503-4806-5 |
DOIs | |
Publication status | Published - 28 Nov 2016 |
Event | 14th International Conference on Advances in Mobile Computing and Multimedia (MoMM 2016) - Singapore, Singapore Duration: 28 Nov 2016 → 30 Nov 2016 http://www.iiwas.org/conferences/momm2016/ |
Publication series
Name | ACM International Conference Proceeding Series |
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Conference
Conference | 14th International Conference on Advances in Mobile Computing and Multimedia (MoMM 2016) |
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Country/Territory | Singapore |
City | Singapore |
Period | 28.11.2016 → 30.11.2016 |
Internet address |
Keywords
- Accelerometer
- gait recognition
- Gaussian Mixture Models
- segmentation
- variance
- Segmentation
- Gaussian mixture models
- Gait recognition
- Variance