  Architectures based on Recurrent Neural Networks (RNNs) have been
successfully applied to many different tasks such as speech or handwriting
recognition with state-of-the-art results. The main contribution of this work
is to analyse the feasibility of RNNs for on-line signature verification in
real practical scenarios. We have considered a system based on Long Short-Term
Memory (LSTM) with a Siamese architecture whose goal is to learn a similarity
metric from pairs of signatures. For the experimental work, the BiosecurID
database comprised of 400 users and 4 separated acquisition sessions are
considered. Our proposed LSTM RNN system has outperformed the results of recent
published works on the BiosecurID benchmark in figures ranging from 17.76% to
28.00% relative verification performance improvement for skilled forgeries.
