Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Choi, Kanghae, Ryu, Hokyoung, Kim, Jieun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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
_version_ 1783692773891768320
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
work_keys_str_mv AT choikanghae deepresidualnetworksforuserauthenticationviahandobjectmanipulations
AT ryuhokyoung deepresidualnetworksforuserauthenticationviahandobjectmanipulations
AT kimjieun deepresidualnetworksforuserauthenticationviahandobjectmanipulations