  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 been established to be convenient 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 intends to explore and propose 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 configurations of the Transformers 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.
