Handwritten signature verification is a significant biometric verification
method widely acknowledged by legal and financial institutions. However, the
development of automatic signature verification systems poses challenges due to
inter-writer similarity, intra-writer variations, and the limited number of
signature samples. To address these challenges, we propose a multiscale global
and regional feature learning network (MGRNet) with the co-tuplet loss, a new
metric learning loss, for offline handwritten signature verification. MGRNet
jointly learns global and regional information from various spatial scales and
integrates it to generate discriminative features. Consequently, it can capture
overall signature stroke information while detecting detailed local differences
between genuine and skilled-forged signatures. To enhance the discriminative
capability of our network further, we propose the co-tuplet loss, which
simultaneously considers multiple positive and negative examples to learn
distance metrics. By dealing with inter-writer similarity and intra-writer
variations and focusing on informative examples, the co-tuplet loss addresses
the limitations of typical metric learning losses. Additionally, we develop
HanSig, a large-scale Chinese signature dataset, to facilitate the development
of robust systems for this script. The dataset is available at
this https URL Experimental results on four benchmark
datasets in different languages demonstrate the promising performance of our
method in comparison to state-of-the-art approaches.

离线手写签名验证中，我们提出了多尺度全局和区域特征学习网络（MGRNet）和共同五元损失（co-tuplet loss）来解决签名相似性、变化性以及样本有限的挑战，同时通过 HanSig 数据集提供了一个稳健系统的大规模中文签名数据集，并且与其他最先进的方法相比，表现出了良好的性能。