首页|基于孪生网络与多重通道融合的脱机笔迹鉴别

基于孪生网络与多重通道融合的脱机笔迹鉴别

扫码查看
原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
脱机签名验证模型因其具有判断签名是否伪造的能力而备受关注.当今大多数脱机签名验证模型可分为深度度量学习方法和双通道判别方法.大部分深度度量学习方法利用孪生网络生成每张图片的细节特征向量,采用欧氏距离法判断相似度,但是欧氏距离仅考虑两个点之间的绝对距离,而容易忽视点的方向、缩放的信息,不会考虑数据之间的相关性,因此无法捕获特征向量内部之间的关系;而双通道判别方法在网络训练前就进行特征的判别,更能判断不同图像的相似性,但此时图像的细节特征不够清晰,大量特征丢失.针对双通道判别方法中特征消失过多的问题,提出了一种面向独立于书写者场景的手写签名离线验证模型 MCFFN(Multi-channel feature fusion network).在 CEDAR、BHSig-B、BHSig-H 和ChiSig四个不同语言的签名数据集上测试了所提出的方法,实验证明了所提方法的优势和潜力.
Offline Handwriting Verification Based on Siamese Network and Multi-channel Fusion
The offline signature verification model has garnered considerable attention due to its ability to discern the authenticity of signatures.Presently,most offline signature verification models can be categorized into deep metric learning approaches and 2-channel discriminative methods.Most of deep metric learning methods use Sia-mese network to generate detailed feature vectors for each image,and the Euclidean distance method is used to de-termine the similarity.However,the Euclidean distance only considers the absolute distance between two points,and it is easy to overlook the direction and scaling information of points.The correlation between data will not be considered,so unable to capture relationships within feature vectors.On the other hand,2-channel discriminative methods perform feature discrimination before the model training,enhancing the ability to determine the dissimilar-ity between different images.However,in this case,the fine details of the images are not sufficiently clear,resulting in a significant loss of features.Addressing the issue of excessive feature loss in 2-channel discriminative methods,this paper introduces a handwritten signature offline verification model designed for scenarios independent of the writer MCFFN(Multi-channel feature fusion network).The efficacy and potential of the proposed method were val-idated through experiments conducted on four distinct language signature datasets:CEDAR,BHSig-B,BHSig-H,and ChiSig.The experimental results affirm the advantages and potential of the proposed approach.

Offline handwritten signature verificationdeep metric learningSiamese networkchannel fusionAC-Mix

林超群、王大寒、肖顺鑫、池雪可、王驰明、张煦尧、朱顺痣

展开 >

厦门理工学院计算机与信息工程学院福建省模式识别与图像理解重点实验室 厦门 361024

中国科学院自动化研究所多模态人工智能系统全国重点实验室 北京 100190

脱机手写签名验证 深度度量学习 孪生网络 通道融合 ACMix

国家自然科学基金国家自然科学基金国家自然科学基金福建省高校产学合作项目福建省技术创新重点攻关及产业化项目福厦泉国家自主创新示范项目国家工信部高技术船舶专项子专题福建省中青年教师教育科研项目

6177332562222609620762362021H60352023XQ0232022FX4CBG4N21-4-4JAT231102

2024

10.16383/j.aas.c230777

自动化学报
中国自动化学会 中国科学院自动化研究所

自动化学报

CSTPCD北大核心
影响因子：1.762
ISSN：0254-4156
年,卷(期)：2024.50(8)
  • 3