Verifying the identity of a person using handwritten signatures is
challenging in the presence of skilled forgeries, where a forger has access to
a person's signature and deliberately attempt to imitate it. In offline
(static) signature verification, the dynamic information of the signature
writing process is lost, and it is difficult to design good feature extractors
that can distinguish genuine signatures and skilled forgeries. This reflects in
a relatively poor performance, with verification errors around 7% in the best
systems in the literature. To address both the difficulty of obtaining good
features, as well as improve system performance, we propose learning the
representations from signature images, in a Writer-Independent format, using
Convolutional Neural Networks. In particular, we propose a novel formulation of
the problem that includes knowledge of skilled forgeries from a subset of users
in the feature learning process, that aims to capture visual cues that
distinguish genuine signatures and forgeries regardless of the user. Extensive
experiments were conducted on four datasets: GPDS, MCYT, CEDAR and Brazilian
PUC-PR datasets. On GPDS-160, we obtained a large improvement in
state-of-the-art performance, achieving 1.72% Equal Error Rate, compared to
6.97% in the literature. We also verified that the features generalize beyond
the GPDS dataset, surpassing the state-of-the-art performance in the other
datasets, without requiring the representation to be fine-tuned to each
particular dataset.

使用卷积神经网络和特征学习过程，以写作人无关的方式对离线签名进行识别，从而提高系统性能和对技艺熟练的伪造的辨别能力。