A multimodal biometric authentication system using of autoencoders and Siamese networks for enhanced security
Kerkeni, Leila (Capgemini engineering (FRança))
Gueuret, Théo (University Gustave Eiffel (França))

Date:	2024
Abstract:	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.
Rights:	Aquest 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.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	User Authentication ; Multimodal Biometrics ; Deep Learning ; Siamese Neural Network ; Autoen-coder ; Face Recognition ; Voice Recognition ; Fusion
Published in:	ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 23 Núm. 1 (2024) , p. 1-14 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.cat/article/view/1811
DOI: 10.5565/rev/elcvia.1811

14 p, 1.2 MB

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