  Deep learning has become a breathtaking technology in the last years,
overcoming traditional handcrafted approaches and even humans for many
different tasks. However, in some tasks, such as the verification of
handwritten signatures, the amount of publicly available data is scarce, what
makes difficult to test the real limits of deep learning. In addition to the
lack of public data, it is not easy to evaluate the improvements of novel
proposed approaches as different databases and experimental protocols are
usually considered.
  The main contributions of this study are: i) we provide an in-depth analysis
of state-of-the-art deep learning approaches for on-line signature
verification, ii) we present and describe the new DeepSignDB on-line
handwritten signature biometric public database, iii) we propose a standard
experimental protocol and benchmark to be used for the research community in
order to perform a fair comparison of novel approaches with the state of the
art, and iv) we adapt and evaluate our recent deep learning approach named
Time-Aligned Recurrent Neural Networks (TA-RNNs) for the task of on-line
handwritten signature verification. This approach combines the potential of
Dynamic Time Warping and Recurrent Neural Networks to train more robust systems
against forgeries. Our proposed TA-RNN system outperforms the state of the art,
achieving results even below 2.0% EER when considering skilled forgery
impostors and just one training signature per user.
