Research on Offline Handwritten Signature Verification explored a large
variety of handcrafted feature extractors, ranging from graphology, texture
descriptors to interest points. In spite of advancements in the last decades,
performance of such systems is still far from optimal when we test the systems
against skilled forgeries - signature forgeries that target a particular
individual. In previous research, we proposed a formulation of the problem to
learn features from data (signature images) in a Writer-Independent format,
using Deep Convolutional Neural Networks (CNNs), seeking to improve performance
on the task. In this research, we push further the performance of such method,
exploring a range of architectures, and obtaining a large improvement in
state-of-the-art performance on the GPDS dataset, the largest publicly
available dataset on the task. In the GPDS-160 dataset, we obtained an Equal
Error Rate of 2.74%, compared to 6.97% in the best result published in
literature (that used a combination of multiple classifiers). We also present a
visual analysis of the feature space learned by the model, and an analysis of
the errors made by the classifier. Our analysis shows that the model is very
effective in separating signatures that have a different global appearance,
while being particularly vulnerable to forgeries that very closely resemble
genuine signatures, even if their line quality is bad, which is the case of
slowly-traced forgeries.

通过使用深度卷积神经网络和 GPDS 数据集，本研究提出了一个更好的离线手写签名验证方法，它在记录一定个人签名技能的伪造签名的情况下取得了 2.74% 的等错误率，并对模型进行了视觉和错误分析。