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Deep Residual Networks for User Authentication via Hand-Object Manipulations
With the ubiquity of wearable devices, various behavioural biometrics have been exploited for continuous user authentication during daily activities. However, biometric authentication using complex hand behaviours have not been sufficiently investigated. This paper presents an implicit and continuou...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8122988/ https://www.ncbi.nlm.nih.gov/pubmed/33922833 http://dx.doi.org/10.3390/s21092981 |
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author | Choi, Kanghae Ryu, Hokyoung Kim, Jieun |
author_facet | Choi, Kanghae Ryu, Hokyoung Kim, Jieun |
author_sort | Choi, Kanghae |
collection | PubMed |
description | With the ubiquity of wearable devices, various behavioural biometrics have been exploited for continuous user authentication during daily activities. However, biometric authentication using complex hand behaviours have not been sufficiently investigated. This paper presents an implicit and continuous user authentication model based on hand-object manipulation behaviour, using a finger-and hand-mounted inertial measurement unit (IMU)-based system and state-of-the-art deep learning models. We employed three convolutional neural network (CNN)-based deep residual networks (ResNets) with multiple depths (i.e., 50, 101, and 152 layers) and two recurrent neural network (RNN)-based long short-term memory (LSTMs): simple and bidirectional. To increase ecological validity, data collection of hand-object manipulation behaviours was based on three different age groups and simple and complex daily object manipulation scenarios. As a result, both the ResNets and LSTMs models acceptably identified users’ hand behaviour patterns, with the best average accuracy of 96.31% and F1-score of 88.08%. Specifically, in the simple hand behaviour authentication scenarios, more layers in residual networks tended to show better performance without showing conventional degradation problems (the ResNet-152 > ResNet-101 > ResNet-50). In a complex hand behaviour scenario, the ResNet models outperformed user authentication compared to the LSTMs. The 152-layered ResNet and bidirectional LSTM showed an average false rejection rate of 8.34% and 16.67% and an equal error rate of 1.62% and 9.95%, respectively. |
format | Online Article Text |
id | pubmed-8122988 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81229882021-05-16 Deep Residual Networks for User Authentication via Hand-Object Manipulations Choi, Kanghae Ryu, Hokyoung Kim, Jieun Sensors (Basel) Article With the ubiquity of wearable devices, various behavioural biometrics have been exploited for continuous user authentication during daily activities. However, biometric authentication using complex hand behaviours have not been sufficiently investigated. This paper presents an implicit and continuous user authentication model based on hand-object manipulation behaviour, using a finger-and hand-mounted inertial measurement unit (IMU)-based system and state-of-the-art deep learning models. We employed three convolutional neural network (CNN)-based deep residual networks (ResNets) with multiple depths (i.e., 50, 101, and 152 layers) and two recurrent neural network (RNN)-based long short-term memory (LSTMs): simple and bidirectional. To increase ecological validity, data collection of hand-object manipulation behaviours was based on three different age groups and simple and complex daily object manipulation scenarios. As a result, both the ResNets and LSTMs models acceptably identified users’ hand behaviour patterns, with the best average accuracy of 96.31% and F1-score of 88.08%. Specifically, in the simple hand behaviour authentication scenarios, more layers in residual networks tended to show better performance without showing conventional degradation problems (the ResNet-152 > ResNet-101 > ResNet-50). In a complex hand behaviour scenario, the ResNet models outperformed user authentication compared to the LSTMs. The 152-layered ResNet and bidirectional LSTM showed an average false rejection rate of 8.34% and 16.67% and an equal error rate of 1.62% and 9.95%, respectively. MDPI 2021-04-23 /pmc/articles/PMC8122988/ /pubmed/33922833 http://dx.doi.org/10.3390/s21092981 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Choi, Kanghae Ryu, Hokyoung Kim, Jieun Deep Residual Networks for User Authentication via Hand-Object Manipulations |
title | Deep Residual Networks for User Authentication via Hand-Object Manipulations |
title_full | Deep Residual Networks for User Authentication via Hand-Object Manipulations |
title_fullStr | Deep Residual Networks for User Authentication via Hand-Object Manipulations |
title_full_unstemmed | Deep Residual Networks for User Authentication via Hand-Object Manipulations |
title_short | Deep Residual Networks for User Authentication via Hand-Object Manipulations |
title_sort | deep residual networks for user authentication via hand-object manipulations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8122988/ https://www.ncbi.nlm.nih.gov/pubmed/33922833 http://dx.doi.org/10.3390/s21092981 |
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