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。