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M-GaitFormer: Mobile biometric gait verification using Transformers

Delgado-Santos, Paula, Tolosana, Ruben, Guest, Richard, Vera-Rodriguez, Ruben, Fierrez, Julian (2023) M-GaitFormer: Mobile biometric gait verification using Transformers. Engineering Applications of Artificial Intelligence, 125 . Article Number 106682. ISSN 0952-1976. (doi:10.1016/j.engappai.2023.106682) (KAR id:102010)


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).

Item Type: Article
DOI/Identification number: 10.1016/j.engappai.2023.106682
Uncontrolled keywords: Biometrics; Behavioural biometrics; Gait verification; Mobile devices; Deep learning; Transformers
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Funders: European Union (
Comunidad de Madrid (
Depositing User: Richard Guest
Date Deposited: 10 Jul 2023 07:02 UTC
Last Modified: 21 Jul 2023 09:58 UTC
Resource URI: (The current URI for this page, for reference purposes)

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