  Passwords are still used on a daily basis for all kind of applications.
However, they are not secure enough by themselves in many cases. This work
enhances password scenarios through two-factor authentication asking the users
to draw each character of the password instead of typing them as usual. The
main contributions of this study are as follows: i) We present the novel
MobileTouchDB public database, acquired in an unsupervised mobile scenario with
no restrictions in terms of position, posture, and devices. This database
contains more than 64K on-line character samples performed by 217 users, with
94 different smartphone models, and up to 6 acquisition sessions. ii) We
perform a complete analysis of the proposed approach considering both
traditional authentication systems such as Dynamic Time Warping (DTW) and novel
approaches based on Recurrent Neural Networks (RNNs). In addition, we present a
novel approach named Time-Aligned Recurrent Neural Networks (TA-RNNs). This
approach combines the potential of DTW and RNNs to train more robust systems
against attacks.
  A complete analysis of the proposed approach is carried out using both
MobileTouchDB and e-BioDigitDB databases. Our proposed TA-RNN system
outperforms the state of the art, achieving a final 2.38% Equal Error Rate,
using just a 4-digit password and one training sample per character. These
results encourage the deployment of our proposed approach in comparison with
traditional typed-based password systems where the attack would have 100%
success rate under the same impostor scenario.
