A multimodal biometric authentication system using of autoencoders and Siamese networks for enhanced security

Ensuring secure and reliable identity verification is crucial, and biometric authentication plays a significant role in achieving this. However, relying on a single biometric trait, unimodal authentication, may have accuracy and attack vulnerability limitations. On the other hand, multimodal authent...

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Detalhes bibliográficos
Autores: Gueuret, Théo, Kerkeni, Leila
Formato: artículo
Fecha de publicación:2024
País:España
Recursos:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:291506
Acesso em linha:https://ddd.uab.cat/record/291506
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1811
Access Level:acceso abierto
Palavra-chave:User Authentication
Multimodal Biometrics
Deep Learning
Siamese Neural Network
Autoen-coder
Face Recognition
Voice Recognition
Fusion
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spelling A multimodal biometric authentication system using of autoencoders and Siamese networks for enhanced securityGueuret, ThéoKerkeni, LeilaUser AuthenticationMultimodal BiometricsDeep LearningSiamese Neural NetworkAutoen-coderFace RecognitionVoice RecognitionFusionEnsuring secure and reliable identity verification is crucial, and biometric authentication plays a significant role in achieving this. However, relying on a single biometric trait, unimodal authentication, may have accuracy and attack vulnerability limitations. On the other hand, multimodal authentication, which combines multiple biometric traits, can enhance accuracy and security by leveraging their complementary strengths. In the literature, different biometric modalities, such as face, voice, fingerprint, and iris, have been studied and used extensively for user authentication. Our research introduces a highly effective multimodal biometric authentication system with a deep learning approach. Our study focuses on two of the most user-friendly safety mechanisms: face and voice recognition. We employ a convolutional autoencoder for face images and an LSTM autoencoder for voice data to extract features. These features are then combined through concatenation to form a joint feature representation. A Siamese network carries out the final step of user identification. We evaluated our model's efficiency using the OMG-Emotion and RAVDESS datasets. We achieved an accuracy of 89.79% and 95% on RAVDESS and OMG-Emotion datasets, respectively. These results are obtained using a combination of face and voice modality. 22024-01-0120242024-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/291506https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1811reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2915062026-06-06T12:50:31Z
dc.title.none.fl_str_mv A multimodal biometric authentication system using of autoencoders and Siamese networks for enhanced security
title A multimodal biometric authentication system using of autoencoders and Siamese networks for enhanced security
spellingShingle A multimodal biometric authentication system using of autoencoders and Siamese networks for enhanced security
Gueuret, Théo
User Authentication
Multimodal Biometrics
Deep Learning
Siamese Neural Network
Autoen-coder
Face Recognition
Voice Recognition
Fusion
title_short A multimodal biometric authentication system using of autoencoders and Siamese networks for enhanced security
title_full A multimodal biometric authentication system using of autoencoders and Siamese networks for enhanced security
title_fullStr A multimodal biometric authentication system using of autoencoders and Siamese networks for enhanced security
title_full_unstemmed A multimodal biometric authentication system using of autoencoders and Siamese networks for enhanced security
title_sort A multimodal biometric authentication system using of autoencoders and Siamese networks for enhanced security
dc.creator.none.fl_str_mv Gueuret, Théo
Kerkeni, Leila
author Gueuret, Théo
author_facet Gueuret, Théo
Kerkeni, Leila
author_role author
author2 Kerkeni, Leila
author2_role author
dc.subject.none.fl_str_mv User Authentication
Multimodal Biometrics
Deep Learning
Siamese Neural Network
Autoen-coder
Face Recognition
Voice Recognition
Fusion
topic User Authentication
Multimodal Biometrics
Deep Learning
Siamese Neural Network
Autoen-coder
Face Recognition
Voice Recognition
Fusion
description Ensuring secure and reliable identity verification is crucial, and biometric authentication plays a significant role in achieving this. However, relying on a single biometric trait, unimodal authentication, may have accuracy and attack vulnerability limitations. On the other hand, multimodal authentication, which combines multiple biometric traits, can enhance accuracy and security by leveraging their complementary strengths. In the literature, different biometric modalities, such as face, voice, fingerprint, and iris, have been studied and used extensively for user authentication. Our research introduces a highly effective multimodal biometric authentication system with a deep learning approach. Our study focuses on two of the most user-friendly safety mechanisms: face and voice recognition. We employ a convolutional autoencoder for face images and an LSTM autoencoder for voice data to extract features. These features are then combined through concatenation to form a joint feature representation. A Siamese network carries out the final step of user identification. We evaluated our model's efficiency using the OMG-Emotion and RAVDESS datasets. We achieved an accuracy of 89.79% and 95% on RAVDESS and OMG-Emotion datasets, respectively. These results are obtained using a combination of face and voice modality.
publishDate 2024
dc.date.none.fl_str_mv 2
2024-01-01
2024
2024-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
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 https://ddd.uab.cat/record/291506
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1811
url https://ddd.uab.cat/record/291506
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1811
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by-nc-nd/4.0/
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
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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