Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data
Walking gait data acquired with force platforms may be used for person re-identification (re-ID) in various authentication, surveillance, and forensics applications. Current force platform-based re-ID systems classify a fixed set of identities (IDs), which presents a problem when IDs are added or re...
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ftdoajarticles:oai:doaj.org/article:7bf32d542c114bfdbeb65998bffd1204 2023-06-06T11:53:09+02:00 Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data Kayne A. Duncanson Simon Thwaites David Booth Gary Hanly William S. P. Robertson Ehsan Abbasnejad Dominic Thewlis 2023-03-01T00:00:00Z https://doi.org/10.3390/s23073392 https://doaj.org/article/7bf32d542c114bfdbeb65998bffd1204 EN eng MDPI AG https://www.mdpi.com/1424-8220/23/7/3392 https://doaj.org/toc/1424-8220 doi:10.3390/s23073392 1424-8220 https://doaj.org/article/7bf32d542c114bfdbeb65998bffd1204 Sensors, Vol 23, Iss 3392, p 3392 (2023) gait recognition biometric ground reaction force deep learning zero-shot re-ID center of pressure Chemical technology TP1-1185 article 2023 ftdoajarticles https://doi.org/10.3390/s23073392 2023-04-16T00:33:18Z Walking gait data acquired with force platforms may be used for person re-identification (re-ID) in various authentication, surveillance, and forensics applications. Current force platform-based re-ID systems classify a fixed set of identities (IDs), which presents a problem when IDs are added or removed from the database. We formulated force platform-based re-ID as a deep metric learning (DML) task, whereby a deep neural network learns a feature representation that can be compared between inputs using a distance metric. The force platform dataset used in this study is one of the largest and the most comprehensive of its kind, containing 193 IDs with significant variations in clothing, footwear, walking speed, and time between trials. Several DML model architectures were evaluated in a challenging setting where none of the IDs were seen during training (i.e., zero-shot re-ID) and there was only one prior sample per ID to compare with each query sample. The best architecture was 85% accurate in this setting, though an analysis of changes in walking speed and footwear between measurement instances revealed that accuracy was 28% higher on same-speed, same-footwear comparisons, compared to cross-speed, cross-footwear comparisons. These results demonstrate the potential of DML algorithms for zero-shot re-ID using force platform data, and highlight challenging cases. Article in Journal/Newspaper DML Directory of Open Access Journals: DOAJ Articles Sensors 23 7 3392 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
gait recognition biometric ground reaction force deep learning zero-shot re-ID center of pressure Chemical technology TP1-1185 |
spellingShingle |
gait recognition biometric ground reaction force deep learning zero-shot re-ID center of pressure Chemical technology TP1-1185 Kayne A. Duncanson Simon Thwaites David Booth Gary Hanly William S. P. Robertson Ehsan Abbasnejad Dominic Thewlis Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data |
topic_facet |
gait recognition biometric ground reaction force deep learning zero-shot re-ID center of pressure Chemical technology TP1-1185 |
description |
Walking gait data acquired with force platforms may be used for person re-identification (re-ID) in various authentication, surveillance, and forensics applications. Current force platform-based re-ID systems classify a fixed set of identities (IDs), which presents a problem when IDs are added or removed from the database. We formulated force platform-based re-ID as a deep metric learning (DML) task, whereby a deep neural network learns a feature representation that can be compared between inputs using a distance metric. The force platform dataset used in this study is one of the largest and the most comprehensive of its kind, containing 193 IDs with significant variations in clothing, footwear, walking speed, and time between trials. Several DML model architectures were evaluated in a challenging setting where none of the IDs were seen during training (i.e., zero-shot re-ID) and there was only one prior sample per ID to compare with each query sample. The best architecture was 85% accurate in this setting, though an analysis of changes in walking speed and footwear between measurement instances revealed that accuracy was 28% higher on same-speed, same-footwear comparisons, compared to cross-speed, cross-footwear comparisons. These results demonstrate the potential of DML algorithms for zero-shot re-ID using force platform data, and highlight challenging cases. |
format |
Article in Journal/Newspaper |
author |
Kayne A. Duncanson Simon Thwaites David Booth Gary Hanly William S. P. Robertson Ehsan Abbasnejad Dominic Thewlis |
author_facet |
Kayne A. Duncanson Simon Thwaites David Booth Gary Hanly William S. P. Robertson Ehsan Abbasnejad Dominic Thewlis |
author_sort |
Kayne A. Duncanson |
title |
Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data |
title_short |
Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data |
title_full |
Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data |
title_fullStr |
Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data |
title_full_unstemmed |
Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data |
title_sort |
deep metric learning for scalable gait-based person re-identification using force platform data |
publisher |
MDPI AG |
publishDate |
2023 |
url |
https://doi.org/10.3390/s23073392 https://doaj.org/article/7bf32d542c114bfdbeb65998bffd1204 |
genre |
DML |
genre_facet |
DML |
op_source |
Sensors, Vol 23, Iss 3392, p 3392 (2023) |
op_relation |
https://www.mdpi.com/1424-8220/23/7/3392 https://doaj.org/toc/1424-8220 doi:10.3390/s23073392 1424-8220 https://doaj.org/article/7bf32d542c114bfdbeb65998bffd1204 |
op_doi |
https://doi.org/10.3390/s23073392 |
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Sensors |
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23 |
container_issue |
7 |
container_start_page |
3392 |
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1767959259504443392 |