  The broad usage of mobile devices nowadays, the sensitiveness of the
information contained in them, and the shortcomings of current mobile user
authentication methods are calling for novel, secure, and unobtrusive solutions
to verify the users' identity. In this article, we propose TypeFormer, a novel
Transformer architecture to model free-text keystroke dynamics performed on
mobile devices for the purpose of user authentication. The proposed model
consists in Temporal and Channel Modules enclosing two Long Short-Term Memory
(LSTM) recurrent layers, Gaussian Range Encoding (GRE), a multi-head
Self-Attention mechanism, and a Block-Recurrent structure. Experimenting on one
of the largest public databases to date, the Aalto mobile keystroke database,
TypeFormer outperforms current state-of-the-art systems achieving Equal Error
Rate (EER) values of 3.25% using only 5 enrolment sessions of 50 keystrokes
each. In such way, we contribute to reducing the traditional performance gap of
the challenging mobile free-text scenario with respect to its desktop and
fixed-text counterparts. Additionally, we analyse the behaviour of the model
with different experimental configurations such as the length of the keystroke
sequences and the amount of enrolment sessions, showing margin for improvement
with more enrolment data. Finally, a cross-database evaluation is carried out,
demonstrating the robustness of the features extracted by TypeFormer in
comparison with existing approaches.
