基于孿生網(wǎng)絡(luò )與多重通道融合的脫機筆跡鑒別
doi: 10.16383/j.aas.c230777
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廈門(mén)理工學(xué)院計算機與信息工程學(xué)院福建省模式識別與圖像理解重點(diǎn)實(shí)驗室 廈門(mén) 361024
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中國科學(xué)院自動(dòng)化研究所多模態(tài)人工智能系統全國重點(diǎn)實(shí)驗室 北京 100190
基金項目: 國家自然科學(xué)基金(61773325, 62222609, 62076236), 福建省高校產(chǎn)學(xué)合作項目(2021H6035), 福建省技術(shù)創(chuàng )新重點(diǎn)攻關(guān)及產(chǎn)業(yè)化項目(2023XQ023), 福廈泉國家自主創(chuàng )新示范項目(2022FX4), 國家工信部高技術(shù)船舶專(zhuān)項子專(zhuān)題(CBG4N21-4-4), 福建省中青年教師教育科研項目(JAT231102)資助
Offline Handwriting Verification Based on Siamese Network and Multi-channel Fusion
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Fujian Key Laboratory of Pattern Recognition and Image Understanding, School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024
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State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
Funds: Supported by National Natural Science Foundation of China (61773325, 62222609, 62076236), Industry-University Cooperation Project of Fujian Science and Technology Department (2021H6035), Fujian Key Technological Innovation and Industrialization Projects (2023XQ023), Fu-Xia-Quan National Independent Innovation Demonstration Project (2022FX4), Type 2030 Green and Intelligent Ship in the Fujian Region (CBG4N21-4-4), and the Education and Scientific Research Projects for Middle-Aged and Young Teachers of Fujian Province (JAT231102)
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摘要: 脫機簽名驗證模型因其具有判斷簽名是否偽造的能力而備受關(guān)注. 當今大多數脫機簽名驗證模型可分為深度度量學(xué)習方法和雙通道判別方法. 大部分深度度量學(xué)習方法利用孿生網(wǎng)絡(luò )生成每張圖片的細節特征向量, 采用歐氏距離法判斷相似度, 但是歐氏距離僅考慮兩個(gè)點(diǎn)之間的絕對距離, 而容易忽視點(diǎn)的方向、縮放的信息, 不會(huì )考慮數據之間的相關(guān)性, 因此無(wú)法捕獲特征向量?jì)炔恐g的關(guān)系; 而雙通道判別方法在網(wǎng)絡(luò )訓練前就進(jìn)行特征的判別, 更能判斷不同圖像的相似性, 但此時(shí)圖像的細節特征不夠清晰, 大量特征丟失. 針對雙通道判別方法中特征消失過(guò)多的問(wèn)題, 提出了一種面向獨立于書(shū)寫(xiě)者場(chǎng)景的手寫(xiě)簽名離線(xiàn)驗證模型MCFFN (Multi-channel feature fusion network). 在 CEDAR、BHSig-B、BHSig-H 和 ChiSig 四個(gè)不同語(yǔ)言的簽名數據集上測試了所提出的方法, 實(shí)驗證明了所提方法的優(yōu)勢和潛力.
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關(guān)鍵詞:
- 脫機手寫(xiě)簽名驗證 /
- 深度度量學(xué)習 /
- 孿生網(wǎng)絡(luò ) /
- 通道融合 /
- ACMix
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. -
表 1 脫機簽名驗證數據集
Table 1 Offline signature verification dataset
數據集名稱(chēng) 語(yǔ)言 簽名種類(lèi) 圖片數量 真實(shí)偽造樣本比 CEDAR 英語(yǔ) 55 2624 24/24 BHSig-B 孟加拉語(yǔ) 100 5400 24/30 BHSig-H 印地語(yǔ) 160 8640 24/30 ChiSig 中文 102 10242 ?/? 
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表 2 基于CEDAR數據集的對比實(shí)驗 (%)
Table 2 Comparison on CEDAR dataset (%)
模型名稱(chēng) 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
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表 3 基于BHSig-B數據集的對比實(shí)驗 (%)
Table 3 Comparison on BHSig-B dataset (%)
模型名稱(chēng) 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
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表 4 基于BHSig-H數據集的對比實(shí)驗 (%)
Table 4 Comparison on BHSig-H dataset (%)
模型名稱(chēng) 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
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表 5 基于ChiSig數據集的消融實(shí)驗 (%)
Table 5 Ablation experiment on ChiSig dataset (%)
模型名稱(chēng) EER TAR ACC InceptionResnet 6.60 28.10 93.60 SigNet — — 82.28 IDN (基線(xiàn)) 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 (無(wú)反灰度圖片, 無(wú)注意力) 11.78 32.49 88.09 OURS (無(wú)反灰度圖片, 單注意力) 10.83 — 89.20 OURS (反灰度圖片, 無(wú)注意力) 7.84 — 92.14 OURS 5.19 28.96 95.23
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表 6 基于ChiSig數據集的主流參數 (%)
Table 6 Main parameters on ChiSig dataset (%)
模型名稱(chēng) 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 (無(wú)反灰度圖片, 無(wú)注意力) 21.91 17.26 88.09 OURS (無(wú)反灰度圖片, 單注意力) 15.59 16.30 89.20 OURS (反灰度圖片, 無(wú)注意力) 6.90 17.18 92.14 OURS 5.34 5.34 95.23
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表 7 跨語(yǔ)言實(shí)驗 (%)
Table 7 Cross-language test (%)
訓練集 測試集 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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