M-GaitFormer: mobile biometric gait verification using transformers
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 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 3.42.90ISIR, respectively, outperforming other state-of-the-art approaches based on popular Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
Biometrics, Behavioural biometrics, Gait verification, Mobile devices, Deep learning, Transformers
Delgado-Santos, Paula
61d96aa4-4228-4b7d-9e55-45737560512e
Tolosana, Ruben
93125127-5ac2-4e76-94aa-4d09f28a3e51
Guest, Richard
93533dbd-b101-491b-83cc-39ccfdc18165
Vera-Rodriguez, Ruben
d9c7e17e-332c-47ac-a9a9-30cc75d26a3e
Fierrez, Julian
c93da818-9beb-4e74-981b-e7a8d394e719
30 June 2023
Delgado-Santos, Paula
61d96aa4-4228-4b7d-9e55-45737560512e
Tolosana, Ruben
93125127-5ac2-4e76-94aa-4d09f28a3e51
Guest, Richard
93533dbd-b101-491b-83cc-39ccfdc18165
Vera-Rodriguez, Ruben
d9c7e17e-332c-47ac-a9a9-30cc75d26a3e
Fierrez, Julian
c93da818-9beb-4e74-981b-e7a8d394e719
Delgado-Santos, Paula, Tolosana, Ruben and Guest, Richard
,
et al.
(2023)
M-GaitFormer: mobile biometric gait verification using transformers.
Engineering Applications of Artificial Intelligence, 125, [106682].
(doi:10.1016/j.engappai.2023.106682).
Abstract
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 3.42.90ISIR, respectively, outperforming other state-of-the-art approaches based on popular Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
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More information
Accepted/In Press date: 18 June 2023
e-pub ahead of print date: 30 June 2023
Published date: 30 June 2023
Keywords:
Biometrics, Behavioural biometrics, Gait verification, Mobile devices, Deep learning, Transformers
Identifiers
Local EPrints ID: 489668
URI: http://eprints.soton.ac.uk/id/eprint/489668
ISSN: 0952-1976
PURE UUID: 2134c11a-82de-43d3-a2aa-e6ef28648b92
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Date deposited: 30 Apr 2024 16:45
Last modified: 01 May 2024 02:10
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Contributors
Author:
Paula Delgado-Santos
Author:
Ruben Tolosana
Author:
Richard Guest
Author:
Ruben Vera-Rodriguez
Author:
Julian Fierrez
Corporate Author: et al.
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