M-GaitFormer: Mobile biometric gait verification using Transformers

Mobile devices such as smartphones and smartwatches are part of our everyday life, acquiring large amount of personal information that needs to be properly secured. Among the different authentication techniques, behavioural biometrics has become a very popular method as it allows authentication in a...

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Detalhes bibliográficos
Autores: Delgado Santos, Paula, Tolosana Moranchel, Rubén, Guest, Richard, Vera Rodríguez, Rubén, Fiérrez Aguilar, Julián
Formato: artículo
Fecha de publicación:2023
País:España
Recursos:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/711866
Acesso em linha:http://hdl.handle.net/10486/711866
https://dx.doi.org/10.1016/j.engappai.2023.106682
Access Level:acceso abierto
Palavra-chave:Behavioural biometrics
Biometrics
Deep learning
Gait verification
Mobile devices
Transformers
Telecomunicaciones
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spelling M-GaitFormer: Mobile biometric gait verification using TransformersDelgado Santos, PaulaTolosana Moranchel, RubénGuest, RichardVera Rodríguez, RubénFiérrez Aguilar, JuliánBehavioural biometricsBiometricsDeep learningGait verificationMobile devicesTransformersTelecomunicacionesMobile devices such as smartphones and smartwatches are part of our everyday life, acquiring large amount of personal information that needs to be properly secured. Among the different authentication techniques, behavioural biometrics has become a very popular method as it allows authentication in a non-intrusive and continuous way. This study proposes M-GaitFormer, a novel mobile biometric gait verification system based on Transformer architectures. This biometric system only considers the accelerometer and gyroscope data acquired by the mobile device. A complete analysis of the proposed M-GaitFormer is carried out using the popular available databases whuGAIT and OU-ISIR. M-GaitFormer achieves Equal Error Rate (EER) values of 3.42% and 2.90% on whuGAIT and OU-ISIR, respectively, outperforming other state-of-the-art approaches based on popular Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860315. With support also from projects INTER-ACTION (PID2021-126521OB-I00 MICINN/FEDER), HumanCAIC (TED2021-131787B-I00 MICINN), and Comunidad de Madrid (ELLIS Unit Madrid)ElsevierDepartamento de Tecnología Electrónica y de las ComunicacionesEscuela Politécnica Superior20232023-06-30research articlehttp://purl.org/coar/resource_type/c_2df8fbb1VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/711866https://dx.doi.org/10.1016/j.engappai.2023.106682reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengEuropean Commission http://dx.doi.org/10.13039/501100000780 Horizon 2020 Framework Programme 860315open accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/7118662026-06-23T12:46:27Z
dc.title.none.fl_str_mv M-GaitFormer: Mobile biometric gait verification using Transformers
title M-GaitFormer: Mobile biometric gait verification using Transformers
spellingShingle M-GaitFormer: Mobile biometric gait verification using Transformers
Delgado Santos, Paula
Behavioural biometrics
Biometrics
Deep learning
Gait verification
Mobile devices
Transformers
Telecomunicaciones
title_short M-GaitFormer: Mobile biometric gait verification using Transformers
title_full M-GaitFormer: Mobile biometric gait verification using Transformers
title_fullStr M-GaitFormer: Mobile biometric gait verification using Transformers
title_full_unstemmed M-GaitFormer: Mobile biometric gait verification using Transformers
title_sort M-GaitFormer: Mobile biometric gait verification using Transformers
dc.creator.none.fl_str_mv Delgado Santos, Paula
Tolosana Moranchel, Rubén
Guest, Richard
Vera Rodríguez, Rubén
Fiérrez Aguilar, Julián
author Delgado Santos, Paula
author_facet Delgado Santos, Paula
Tolosana Moranchel, Rubén
Guest, Richard
Vera Rodríguez, Rubén
Fiérrez Aguilar, Julián
author_role author
author2 Tolosana Moranchel, Rubén
Guest, Richard
Vera Rodríguez, Rubén
Fiérrez Aguilar, Julián
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Tecnología Electrónica y de las Comunicaciones
Escuela Politécnica Superior
dc.subject.none.fl_str_mv Behavioural biometrics
Biometrics
Deep learning
Gait verification
Mobile devices
Transformers
Telecomunicaciones
topic Behavioural biometrics
Biometrics
Deep learning
Gait verification
Mobile devices
Transformers
Telecomunicaciones
description Mobile devices such as smartphones and smartwatches are part of our everyday life, acquiring large amount of personal information that needs to be properly secured. Among the different authentication techniques, behavioural biometrics has become a very popular method as it allows authentication in a non-intrusive and continuous way. This study proposes M-GaitFormer, a novel mobile biometric gait verification system based on Transformer architectures. This biometric system only considers the accelerometer and gyroscope data acquired by the mobile device. A complete analysis of the proposed M-GaitFormer is carried out using the popular available databases whuGAIT and OU-ISIR. M-GaitFormer achieves Equal Error Rate (EER) values of 3.42% and 2.90% on whuGAIT and OU-ISIR, respectively, outperforming other state-of-the-art approaches based on popular Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-06-30
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/711866
https://dx.doi.org/10.1016/j.engappai.2023.106682
url http://hdl.handle.net/10486/711866
https://dx.doi.org/10.1016/j.engappai.2023.106682
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://dx.doi.org/10.13039/501100000780 Horizon 2020 Framework Programme 860315
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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