Exploring transformers for behavioural biometrics: a case study in gait recognition

Biometrics on mobile devices has attracted a lot of attention in recent years as it is considered a user-friendly authentication method. This interest has also been motivated by the success of Deep Learning (DL). Architectures based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networ...

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Detalles Bibliográficos
Autores: Delgado Santos, Paula, Tolosana Moranchel, Rubén, Guest, Richard, Deravi, Farzin, Vera Rodríguez, Rubén
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/711658
Acceso en línea:http://hdl.handle.net/10486/711658
https://dx.doi.org/10.1016/j.patcog.2023.109798
Access Level:acceso abierto
Palabra clave:Behavioural biometrics
Biometrics
Deep learning
Gait recognition
Mobile devices
Transformers
Telecomunicaciones
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spelling Exploring transformers for behavioural biometrics: a case study in gait recognitionDelgado Santos, PaulaTolosana Moranchel, RubénGuest, RichardDeravi, FarzinVera Rodríguez, RubénBehavioural biometricsBiometricsDeep learningGait recognitionMobile devicesTransformersTelecomunicacionesBiometrics on mobile devices has attracted a lot of attention in recent years as it is considered a user-friendly authentication method. This interest has also been motivated by the success of Deep Learning (DL). Architectures based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have established convenience for the task, improving the performance and robustness in comparison to traditional machine learning techniques. However, some aspects must still be revisited and improved. To the best of our knowledge, this is the first article that explores and proposes a novel gait biometric recognition systems based on Transformers, which currently obtain state-of-the-art performance in many applications. Several state-of-the-art architectures (Vanilla, Informer, Autoformer, Block-Recurrent Transformer, and THAT) are considered in the experimental framework. In addition, new Transformer configurations are proposed to further increase the performance. Experiments are carried out using the two popular public databases: whuGAIT and OU-ISIR. The results achieved prove the high ability of the proposed Transformer, outperforming state-of-the-art CNN and RNN architecturesThis 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) and HumanCAIC (TED2021-131787B-I00 MICINN), and from Comunidad de Madrid (ELLIS Unit Madrid)ElsevierDepartamento de Tecnología Electrónica y de las ComunicacionesEscuela Politécnica Superior20232023-07-04research articlehttp://purl.org/coar/resource_type/c_2df8fbb1VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/711658https://dx.doi.org/10.1016/j.patcog.2023.109798reponame: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:10486/7116582026-06-23T12:46:27Z
dc.title.none.fl_str_mv Exploring transformers for behavioural biometrics: a case study in gait recognition
title Exploring transformers for behavioural biometrics: a case study in gait recognition
spellingShingle Exploring transformers for behavioural biometrics: a case study in gait recognition
Delgado Santos, Paula
Behavioural biometrics
Biometrics
Deep learning
Gait recognition
Mobile devices
Transformers
Telecomunicaciones
title_short Exploring transformers for behavioural biometrics: a case study in gait recognition
title_full Exploring transformers for behavioural biometrics: a case study in gait recognition
title_fullStr Exploring transformers for behavioural biometrics: a case study in gait recognition
title_full_unstemmed Exploring transformers for behavioural biometrics: a case study in gait recognition
title_sort Exploring transformers for behavioural biometrics: a case study in gait recognition
dc.creator.none.fl_str_mv Delgado Santos, Paula
Tolosana Moranchel, Rubén
Guest, Richard
Deravi, Farzin
Vera Rodríguez, Rubén
author Delgado Santos, Paula
author_facet Delgado Santos, Paula
Tolosana Moranchel, Rubén
Guest, Richard
Deravi, Farzin
Vera Rodríguez, Rubén
author_role author
author2 Tolosana Moranchel, Rubén
Guest, Richard
Deravi, Farzin
Vera Rodríguez, Rubén
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 recognition
Mobile devices
Transformers
Telecomunicaciones
topic Behavioural biometrics
Biometrics
Deep learning
Gait recognition
Mobile devices
Transformers
Telecomunicaciones
description Biometrics on mobile devices has attracted a lot of attention in recent years as it is considered a user-friendly authentication method. This interest has also been motivated by the success of Deep Learning (DL). Architectures based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have established convenience for the task, improving the performance and robustness in comparison to traditional machine learning techniques. However, some aspects must still be revisited and improved. To the best of our knowledge, this is the first article that explores and proposes a novel gait biometric recognition systems based on Transformers, which currently obtain state-of-the-art performance in many applications. Several state-of-the-art architectures (Vanilla, Informer, Autoformer, Block-Recurrent Transformer, and THAT) are considered in the experimental framework. In addition, new Transformer configurations are proposed to further increase the performance. Experiments are carried out using the two popular public databases: whuGAIT and OU-ISIR. The results achieved prove the high ability of the proposed Transformer, outperforming state-of-the-art CNN and RNN architectures
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-07-04
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/10486/711658
https://dx.doi.org/10.1016/j.patcog.2023.109798
url http://hdl.handle.net/10486/711658
https://dx.doi.org/10.1016/j.patcog.2023.109798
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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score 15,300724

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