Handwritten signature verification poses a formidable challenge in biometrics
and document authenticity. The objective is to ascertain the authenticity of a
provided handwritten signature, distinguishing between genuine and forged ones.
This issue has many applications in sectors such as finance, legal
documentation, and security. Currently, the field of computer vision and
machine learning has made significant progress in the domain of handwritten
signature verification. The outcomes, however, may be enhanced depending on the
acquired findings, the structure of the datasets, and the used models. Four
stages make up our suggested strategy. First, we collected a large dataset of
12600 images from 420 distinct individuals, and each individual has 30
signatures of a certain kind (All authors signatures are genuine). In the
subsequent stage, the best features from each image were extracted using a deep
learning model named MobileNetV2. During the feature selection step, three
selectors neighborhood component analysis (NCA), Chi2, and mutual info (MI)
were used to pull out 200, 300, 400, and 500 features, giving a total of 12
feature vectors. Finally, 12 results have been obtained by applying machine
learning techniques such as SVM with kernels (rbf, poly, and linear), KNN, DT,
Linear Discriminant Analysis, and Naive Bayes. Without employing feature
selection techniques, our suggested offline signature verification achieved a
classification accuracy of 91.3%, whereas using the NCA feature selection
approach with just 300 features it achieved a classification accuracy of 97.7%.
High classification accuracy was achieved using the designed and suggested
model, which also has the benefit of being a self-organized framework.
Consequently, using the optimum minimally chosen features, the proposed method
could identify the best model performance and result validation prediction
vectors.

手写签名验证是生物识别和文件真实性方面的严峻挑战，最优特征选择和分类准确度显著提高了提出的离线签名验证方法的性能和结果验证预测向量。

离线手写签名验证：迁移学习与特征选择方法

Offline Handwriting Signature Verification: A Transfer Learning and  Feature Selection Approach

This paper introduces a novel approach to leverage the knowledge of existing
expert models for training new Convolutional Neural Networks, on domains where
task-specific data are limited or unavailable. The presented scheme is applied
in offline handwritten signature verification (OffSV) which, akin to other
biometric applications, suffers from inherent data limitations due to
regulatory restrictions. The proposed Student-Teacher (S-T) configuration
utilizes feature-based knowledge distillation (FKD), combining graph-based
similarity for local activations with global similarity measures to supervise
student's training, using only handwritten text data. Remarkably, the models
trained using this technique exhibit comparable, if not superior, performance
to the teacher model across three popular signature datasets. More importantly,
these results are attained without employing any signatures during the feature
extraction training process. This study demonstrates the efficacy of leveraging
existing expert models to overcome data scarcity challenges in OffSV and
potentially other related domains.

该研究引入了一种新方法，在任务特定数据有限或不可用的领域中，利用现有专家模型的知识来训练新的卷积神经网络。该方案应用于脱机手写签名验证（OffSV），通过特征为基础的知识蒸馏（FKD）和全局相似性度量，仅使用手写文本数据来监督学生的训练，从而使得训练出的模型在三个常用签名数据集中达到与或超过教师模型的性能，而且在特征提取训练过程中无需使用任何签名，从而证明了利用现有专家模型克服 OffSV 和其他相关领域的数据稀缺性挑战的功效。

在数据稀缺领域中利用专家模型训练深度神经网络：应用于离线手写签名验证

Leveraging Expert Models for Training Deep Neural Networks in Scarce  Data Domains: Application to Offline Handwritten Signature Verification

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。