  Automatic Offline Handwritten Signature Verification has been researched over
the last few decades from several perspectives, using insights from graphology,
computer vision, signal processing, among others. In spite of the advancements
on the field, building classifiers that can separate between genuine signatures
and skilled forgeries (forgeries made targeting a particular signature) is
still hard. We propose approaching the problem from a feature learning
perspective. Our hypothesis is that, in the absence of a good model of the data
generation process, it is better to learn the features from data, instead of
using hand-crafted features that have no resemblance to the signature
generation process. To this end, we use Deep Convolutional Neural Networks to
learn features in a writer-independent format, and use this model to obtain a
feature representation on another set of users, where we train writer-dependent
classifiers. We tested our method in two datasets: GPDS-960 and Brazilian
PUC-PR. Our experimental results show that the features learned in a subset of
the users are discriminative for the other users, including across different
datasets, reaching close to the state-of-the-art in the GPDS dataset, and
improving the state-of-the-art in the Brazilian PUC-PR dataset.
