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              基于孿生網絡與多重通道融合的脫機筆跡鑒別

              林超群 王大寒 肖順鑫 池雪可 王馳明 張煦堯 朱順痣

              林超群, 王大寒, 肖順鑫, 池雪可, 王馳明, 張煦堯, 朱順痣. 基于孿生網絡與多重通道融合的脫機筆跡鑒別. 自動化學報, xxxx, xx(x): x?xx doi: 10.16383/j.aas.c230777
              引用本文: 林超群, 王大寒, 肖順鑫, 池雪可, 王馳明, 張煦堯, 朱順痣. 基于孿生網絡與多重通道融合的脫機筆跡鑒別. 自動化學報, xxxx, xx(x): x?xx doi: 10.16383/j.aas.c230777
              Lin Chao-Qun, Wang Da-Han, Xiao Shun-Xin, Chi Xue-Ke, Wang Chi-Ming, Zhang Xu-Yao, Zhu Shun-Zhi. Offline handwriting verification based on siamese network and multi-channel fusion. Acta Automatica Sinica, xxxx, xx(x): x?xx doi: 10.16383/j.aas.c230777
              Citation: Lin Chao-Qun, Wang Da-Han, Xiao Shun-Xin, Chi Xue-Ke, Wang Chi-Ming, Zhang Xu-Yao, Zhu Shun-Zhi. Offline handwriting verification based on siamese network and multi-channel fusion. Acta Automatica Sinica, xxxx, xx(x): x?xx doi: 10.16383/j.aas.c230777

              基于孿生網絡與多重通道融合的脫機筆跡鑒別

              doi: 10.16383/j.aas.c230777
              基金項目: 國家自然科學基金項目(61773325, 62222609, 62076236), 福建省高校產學合作項目(2021H6035), 福建省技術創新重點攻關及產業化項目(2023XQ023), 福廈泉國家自主創新示范項目(2022FX4), 國家工信部高技術船舶專項子專題(CBG4N21-4-4), 福建省中青年教師教育科研項目(JAT231102)資助
              詳細信息
                作者簡介:

                林超群:廈門理工學院計算機與信息工程學院碩士研究生. 主要研究方向為脫機筆跡鑒別. E-mail: lincq@s.xmut.edu.cn

                王大寒:廈門理工學院計算機與信息工程學院教授, 研究員. 2012年獲中國科學院大學博士學位.主要研究方向為模式識別, 計算機視覺, 深度學習. 本文通信作者. E-mail: wangdh@xmut.edu.cn

                肖順鑫:廈門理工學院計算機與信息工程學院講師. 2023年獲福州大學博士學位. 主要研究方向為圖神經網絡, 表示學習, 生物信息計算, 可信人工智能. E-mail: xiaoshunxin.tj@gmail.com

                池雪可:2022年獲得廈門理工學院碩士學位. 主要研究方向為計算機視覺, 手寫數學公式識別. E-mail: 13213834013@163.com

                王馳明:廈門理工學院計算機與信息工程學院講師. 2020年獲廈門大學博士學位. 主要研究方向為船舶智能運維, 聲振感知. E-mail: wangchiming009@163.com

                張煦堯:中國科學院自動化研究所研究員. 2013年獲中國科學院大學博士學位. 主要研究方向為模式識別,機器學習和文字識別. E-mail: xyz@nlpr.ia.ac.cn

                朱順痣:廈門理工學院計算機與信息工程學院教授. 2007年獲廈門大學博士學位. 主要研究方向為數據挖掘, 視頻分析與處理, 信息推薦, 系統工程. E-mail: szzhu@xmut.edu.cn

              • 中圖分類號: Y

              Offline Handwriting Verification Based on Siamese Network and Multi-channel Fusion

              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), the Education and Scientific Research Projects for Middle-Aged and Young Teachers of Fujian Province (JAT231102)
              More Information
                Author Bio:

                LIN Chao-Qun Master student at the School of Computer and Information Engineering, Xiamen University of Technology His research interest covers Offline signature verification

                WANG Da-Han Professor and Researcher, School of Computer and Information Engineering, Xiamen University of Technology. He received his Ph.D. degree from the University of Chinese Academy of Sciences in 2012. His research interest covers pattern recognition, computer vision, and deep learning. Corresponding author of this paper

                XIAO Shun-Xin  Lecturer at the School of Computer and Information Engineering, Xiamen University of Technology. He received his Ph.D. degree from the Fuzhou University in 2023. His research interest covers graph neural networks, representation learning, bioinformatics computing, and trusted artificial intelligence

                Chi Xue-Ke Received Master degree from Xiamen University of Technology. Her research interest covers computer vision and handwritten mathematical formula recognition

                WANG Chi-Ming Lecturer at the School of Computer and Information Engineering, Xiamen University of Technology. He received his Ph.D. degree from the Xiamen University in 2020. His research interest covers intelligent operation and maintenance of ships, sound and vibration perception

                ZHANG Xu-Yao Professor at the Institute of Automation, Chinese Academy of Sciences. He received his Ph.D. degree from the University of Chinese Academy of Sciences in 2013. His research interest covers pattern recognition, machine learning, and handwriting recognition

                ZHU Shun-Zhi Professor at School of Computer and Information Engineering, Xiamen University of Technology. He received his Ph.D. degree from the Xiamen University in 2007. His research interest covers data mining, video analysis and processing, information recommendation, and systems engineering

              • 摘要: 脫機簽名驗證模型因其判斷簽名是否偽造的能力而備受關注. 當今大多數脫機簽名驗證模型可分為深度度量學習方法和雙通道判別方法. 大部分深度度量學習方法利用孿生網絡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點之間的絕對距離, 而容易忽視點的方向、縮放的信息, 不會考慮數據之間的相關性, 因此無法捕獲特征向量內部之間的關系; 而雙通道判別方法在網絡前就進行特征的判別, 更能判斷不同圖像的相似性, 但此時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多的問題, 提出了一種面向獨立于書寫者場景的手寫簽名離線驗證模型(Multi-channel feature fusion network, MCFFN). 在 CEDAR、BHSig-B、BHSig-H 和 ChiSig 四個不同語言的簽名數據集上測試了所提出的方法, 實驗證明了所提方法的優勢和潛力.
              • 圖  1  孿生網絡結構圖

                Fig.  1  Structure of siamese network

                圖  2  雙通道網絡圖

                Fig.  2  Structure of 2-channel network

                圖  3  MCFFN網絡結構圖

                Fig.  3  Structure of MCFFN

                圖  4  雙重逆鑒別注意力模塊

                Fig.  4  Dual reverse forensic attention module

                圖  5  注意力特征圖

                Fig.  5  Attentional characteristics map

                表  1  脫機簽名驗證數據集

                Table  1  Offline signature verification dataset

                數據集名稱 語言 簽名種類 圖片數量 真實偽造樣本比
                CEDAR 英語 55 2624 24/24
                BHSig260-B 孟加拉語 100 5400 24/30
                BHSig260-H 印地語 160 8640 24/30
                ChiSig 中文 102 10242 ?/?
                下載: 導出CSV

                表  2  基于CEDAR數據集的對比實驗

                Table  2  Comparison on CEDAR dataset

                模型名稱 FRR FAR ACC
                SigNet (2017arXiv) 0 0 100
                DeepHSV (2019ICDAR) 100
                IDN (2019CVPR) 2.17 5.87 96.38
                SDINet (2021AAAI) 3.42 0.73 98.25
                2C2S (2023EAAI) 0 0 100
                OURS 0 0 100
                下載: 導出CSV

                表  3  基于BHSig-B數據集的對比實驗

                Table  3  Comparison on BHSig-B dataset

                模型名稱 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
                下載: 導出CSV

                表  4  基于BHSig-H數據集的對比實驗

                Table  4  Comparison on BHSig-H dataset

                模型名稱 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 95.7
                下載: 導出CSV

                表  5  基于ChiSig數據集的消融實驗

                Table  5  Ablation experiment on ChiSig dataset

                模型名稱 EER TAR ACC
                InceptionResnet 6.6 28.1 93.6
                SigNet 82.28
                IDN (基線) 17.91 10.5 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 (無反灰度圖片, 無注意力) 11.78 32.49 88.09
                OURS (無反灰度圖片, 單注意力) 10.83 89.20
                OURS (反灰度圖片, 無注意力) 7.84 92.14
                OURS 5.19 28.96 95.23
                下載: 導出CSV

                表  6  基于ChiSig數據集的主流參數

                Table  6  Main parameter on ChiSig dataset

                模型名稱 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 (無反灰度圖片, 無注意力) 21.91 17.26 88.09
                OURS (無反灰度圖片, 單注意力) 15.59 16.30 89.20
                OURS (反灰度圖片, 無注意力) 6.90 17.18 92.14
                OURS 5.34 5.34 95.23
                下載: 導出CSV

                表  7  跨語言實驗

                Table  7  Cross-language test

                訓練集/測試集 CEDAR BHSig-B BHSig-H ChiSig
                CEDAR 100 48.76 49.89 57.48
                BHSig-B 64.86 95.61 82.79 63.71
                BHSig-H 50.11 86.27 95.7 20
                ChiSig 54.60 70.02 55.37 95.23
                下載: 導出CSV
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