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

Biometrics on mobile devices has attracted a lot of attention in recent years as it is considered a userfriendly 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.& COPY; 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

Automated summary

Biometrics on mobile devices has gained attention as a user-friendly authentication method, especially with the success of Deep Learning. Architectures based on CNNs and RNNs have improved performance and robustness compared to traditional machine learning techniques. This article proposes a novel gait biometric recognition system based on Transformers and achieves state-of-the-art performance, outperforming CNN and RNN architectures.