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。

使用卷积神经网络学习表示的离线签名验证的共识阈值准则

Consensus-Threshold Criterion for Offline Signature Verification using  Convolutional Neural Network Learned Representations

Unsupervised learning for geometric perception (depth, optical flow, etc.) is
of great interest to autonomous systems. Recent works on unsupervised learning
have made considerable progress on perceiving geometry; however, they usually
ignore the coherence of objects and perform poorly under scenarios with dark
and noisy environments. In contrast, supervised learning algorithms, which are
robust, require large labeled geometric dataset. This paper introduces SIGNet,
a novel framework that provides robust geometry perception without requiring
geometrically informative labels. Specifically, SIGNet integrates semantic
information to make depth and flow predictions consistent with objects and
robust to low lighting conditions. SIGNet is shown to improve upon the
state-of-the-art unsupervised learning for depth prediction by 30% (in squared
relative error). In particular, SIGNet improves the dynamic object class
performance by 39% in depth prediction and 29% in flow prediction. Our code
will be made available at this https URL

本文介绍了 SIGNet，这是一个新的框架，可以提供强大的几何感知，而无需几何信息的标签，SIGNet 通过使用语义信息来使深度和流的预测与物体一致，并使其在低照条件下具有稳健性。SIGNet 在深度预测方面比最先进的无监督学习方法提高了 30％。

SIGNet: 基于语义实例辅助的无监督三维几何感知

SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception

Offline signature verification is one of the most challenging tasks in
biometrics and document forensics. Unlike other verification problems, it needs
to model minute but critical details between genuine and forged signatures,
because a skilled falsification might often resembles the real signature with
small deformation. This verification task is even harder in writer independent
scenarios which is undeniably fiscal for realistic cases. In this paper, we
model an offline writer independent signature verification task with a
convolutional Siamese network. Siamese networks are twin networks with shared
weights, which can be trained to learn a feature space where similar
observations are placed in proximity. This is achieved by exposing the network
to a pair of similar and dissimilar observations and minimizing the Euclidean
distance between similar pairs while simultaneously maximizing it between
dissimilar pairs. Experiments conducted on cross-domain datasets emphasize the
capability of our network to model forgery in different languages (scripts) and
handwriting styles. Moreover, our designed Siamese network, named SigNet,
exceeds the state-of-the-art results on most of the benchmark signature
datasets, which paves the way for further research in this direction.

使用卷积 Siamese 网络，Modi 等人提出了一种离线笔迹验证的方法，其在多语言脚本和手写风格的模拟中，能够显著优于现有数据集上的最新结果，这为进一步研究提供了奠定基础。