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

Delgado-Santos, Paula, Tolosana, Ruben, Guest, Richard, Deravi, Farzin, Vera-Rodriguez, Ruben (2023) Exploring transformers for behavioural biometrics: A case study in gait recognition. Pattern Recognition, 143 . Article Number 109798. ISSN 0031-3203. (doi:10.1016/j.patcog.2023.109798) (KAR id:101946)


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.

Item Type: Article
DOI/Identification number: 10.1016/j.patcog.2023.109798
Uncontrolled keywords: Biometrics; Behavioural biometrics; Gait recognition; Deep learningTransformers; Mobile devices
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 (
Depositing User: Richard Guest
Date Deposited: 10 Jul 2023 06:57 UTC
Last Modified: 11 Jan 2024 09:25 UTC
Resource URI: (The current URI for this page, for reference purposes)

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