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摘要: 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.Abstract: 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 Siamese network to generate detailed feature vectors for each image, and the Euclidean distance method is used to determine 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 dissimilarity 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 validated 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.
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表 1 脱机签名验证数据集
Table 1 Offline signature verification dataset
数据集名称 语言 签名种类 图片数量 真实伪造样本比 CEDAR 英语 55 2624 24/24 BHSig-B 孟加拉语 100 5400 24/30 BHSig-H 印地语 160 8640 24/30 ChiSig 中文 102 10242 −/− 表 2 基于CEDAR数据集的对比实验 (%)
Table 2 Comparison on CEDAR dataset (%)
模型名称 FRR FAR ACC SigNet (2017arXiv) 0 0 100.00 DeepHSV (2019ICDAR) — — 100.00 IDN (2019CVPR) 2.17 5.87 96.38 SDINet (2021AAAI) 3.42 0.73 98.25 2C2S (2023EAAI) 0 0 100.00 OURS 0 0 100.00 表 3 基于BHSig-B数据集的对比实验 (%)
Table 3 Comparison on BHSig-B dataset (%)
模型名称 FRR FAR ACC SigNet (2017arXiv) 13.89 13.89 86.11 DeepHSV (2019ICDAR) — — 88.08 IDN (2019CVPR) 5.24 4.12 95.32 SDINet (2021AAAI) 7.86 3.30 94.42 SURDS (2022ICPR) 5.42 19.89 87.34 2C2S (2023EAAI) 8.11 5.37 93.25 TransOSV (2022ICME) 9.95 9.95 90.05 OURS 3.86 3.84 95.61 表 4 基于BHSig-H数据集的对比实验 (%)
Table 4 Comparison on BHSig-H dataset (%)
模型名称 FRR FAR ACC SigNet (2017arXiv) 15.36 15.36 84.64 DeepHSV (2019ICDAR) — — 86.66 IDN (2019CVPR) 4.93 8.99 93.04 SDINet (2021AAAI) 3.77 6.24 95.00 SURDS (2022ICPR) 8.98 12.01 89.50 2C2S (2023EAAI) 9.98 8.66 90.68 TransOSV (2022ICME) 3.39 3.39 96.61 OURS 4.89 4.89 95.70 表 5 基于ChiSig数据集的消融实验 (%)
Table 5 Ablation experiment on ChiSig dataset (%)
模型名称 EER TAR ACC InceptionResnet 6.60 28.10 93.60 SigNet — — 82.28 IDN (基线) 17.91 10.50 84.82 IDN (通道融合) 14.81 9.61 85.72 IDN (通道融合 + 注意力) 11.38 7.82 88.96 OURS (无反灰度图片, 无注意力) 11.78 32.49 88.09 OURS (无反灰度图片, 单注意力) 10.83 — 89.20 OURS (反灰度图片, 无注意力) 7.84 — 92.14 OURS 5.19 28.96 95.23 表 6 基于ChiSig数据集的主流参数 (%)
Table 6 Main parameters on ChiSig dataset (%)
模型名称 FRR FAR ACC IDN 10.46 17.91 84.82 IDN (通道融合) 9.61 18.97 85.72 IDN (通道融合 + 注意力) 7.82 14.27 88.96 OURS (无反灰度图片, 无注意力) 21.91 17.26 88.09 OURS (无反灰度图片, 单注意力) 15.59 16.30 89.20 OURS (反灰度图片, 无注意力) 6.90 17.18 92.14 OURS 5.34 5.34 95.23 表 7 跨语言实验 (%)
Table 7 Cross-language test (%)
训练集 测试集 CEDAR BHSig-B BHSig-H ChiSig CEDAR 100.00 48.76 49.89 57.48 BHSig-B 64.86 95.61 82.79 63.71 BHSig-H 50.11 86.27 95.70 20.00 ChiSig 54.60 70.02 55.37 95.23 -
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