Signature verification is a critical task in many applications, including
forensic science, legal judgments, and financial markets. However, current
signature verification systems are often difficult to explain, which can limit
their acceptance in these applications. In this paper, we propose a novel
explainable offline automatic signature verifier (ASV) to support forensic
handwriting examiners. Our ASV is based on a universal background model (UBM)
constructed from offline signature images. It allows us to assign a questioned
signature to the UBM and to a reference set of known signatures using simple
distance measures. This makes it possible to explain the verifier's decision in
a way that is understandable to non experts. We evaluated our ASV on publicly
available databases and found that it achieves competitive performance with
state of the art ASVs, even when challenging 1 versus 1 comparison are
considered. Our results demonstrate that it is possible to develop an
explainable ASV that is also competitive in terms of performance. We believe
that our ASV has the potential to improve the acceptance of signature
verification in critical applications such as forensic science and legal
judgments.

我们提出了一种新颖的可解释的离线自动签名验证器 (ASV)，它基于由离线签名图像构建的通用背景模型 (UBM)。我们的 ASV 能够将质疑的签名与 UBM 和已知签名的参考集进行简单距离度量，从而解释验证器的决策，使其易于非专家理解，并且在性能方面具有竞争力。我们相信我们的 ASV 有潜力提高签名验证在法医科学和法律判决等关键应用领域的接受程度。

可解释的离线自动签名验证器以支持法证书写鉴定人员

Explainable offline automatic signature verifier to support forensic  handwriting examiners

Building upon findings in computational model of handwriting learning and
execution, we introduce the concept of stability to explain the difference
between the actual movements performed during multiple execution of the
subject's signature, and conjecture that the most stable parts of the signature
should play a paramount role in evaluating the similarity between a questioned
signature and the reference ones during signature verification. We then
introduce the Stability Modulated Dynamic Time Warping algorithm for
incorporating the stability regions, i.e. the most similar parts between two
signatures, into the distance measure between a pair of signatures computed by
the Dynamic Time Warping for signature verification. Experiments were conducted
on two datasets largely adopted for performance evaluation. Experimental
results show that the proposed algorithm improves the performance of the
baseline system and compares favourably with other top performing signature
verification systems.

基于计算模型的书写学习和执行研究结果，我们引入了稳定性的概念来解释被试的签名在多次执行过程中实际运动的差异，并推测在签名验证过程中，签名的最稳定部分应该在评估被质疑签名与参考签名之间的相似性方面起着重要作用。我们随后引入了稳定区域调制动态时间规整算法，将稳定区域（即两个签名之间最相似的部分）纳入由动态时间规整算法计算的一对签名之间的距离度量中，用于签名验证。我们在两个用于性能评估的数据集上进行了实验。实验结果显示，所提出的算法改进了基准系统的性能，并与其他表现优异的签名验证系统相比具有可比性。

SM-DTW：用于签名验证的稳定调节的动态时间规整

SM-DTW: Stability Modulated Dynamic Time Warping for signature  verification

A genuine signer's signature is naturally unstable even at short
time-intervals whereas, expert forgers always try to perfectly mimic a genuine
signer's signature. This presents a challenge which puts a genuine signer at
risk of being denied access, while a forge signer is granted access. The
implication is a high false acceptance rate (FAR) which is the percentage of
forge signature classified as belonging to a genuine class. Existing work have
only scratched the surface of signature verification because the
misclassification error remains high. In this paper, a consensus-threshold
distance-based classifier criterion is proposed for offline writer-dependent
signature verification. Using features extracted from SigNet and SigNet-F deep
convolutional neural network models, the proposed classifier minimizes FAR.
This is demonstrated via experiments on four datasets: GPDS-300, MCYT, CEDAR
and Brazilian PUC-PR datasets. On GPDS-300, the consensus threshold classifier
improves the state-of-the-art performance by achieving a 1.27% FAR compared to
8.73% and 17.31% recorded in literature. This performance is consistent across
other datasets and guarantees that the risk of imposters gaining access to
sensitive documents or transactions is minimal.

提出了一种基于一致性阈值距离分类器准则的离线依赖作者的签名验证方法，通过 SigNet 和 SigNet-F 深度卷积神经网络模型提取的特征，最小化了假接受率，并在多个数据集上进行了实验证明了该方法相比现有的文献成果，在减小误分类错误的同时，将冒充者获取敏感文档或交易的风险降到最低。