使用二階段度量學習的中文離線簽名辨識

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姓名

黃重霖(Chong-Lin Huang) 查詢紙本館藏

畢業系所

資訊工程學系在職專班

論文名稱

使用二階段度量學習的中文離線簽名辨識
(Using two-stage metric learning for Chinese offline signature recognition)

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至系統瀏覽論文 (2028-6-15以後開放)

摘要(中)

本研究旨在解決人工識別簽名所需的龐大人力與時間成本，以及如何準確、快速檢測偽造簽名的問題。我們提出了一個能夠在辨識中文離線簽名方面表現良好的系統，該系統通過對簽名資料集進行資料前處理，結合現有的深度學習技術，通過二階段度量學習和三元組損失函數對資料集進行模型訓練，最終，我們利用該模型預測輸入簽名影像的相似度距離。在實際應用的簽名例子中，我們實現了平均偵測仿冒準確率為 82%的結果。此一成果可以應用於大型考試的自動化簽名辨識。

摘要(英)

This study aims to address the significant manpower and time costs required for manual identification of signatures, as well as the issue of accurately and quickly detecting forged signatures. We propose a system that performs well in the recognition of Chinese offline signatures. The system preprocesses the signature dataset, combines existing deep learning techniques, trains the dataset using two-stage metric learning and the triplet loss function, and ultimately uses the model to predict the similarity distance of input signature images. In practical signature examples, we achieved an average detection accuracy of 82% for detecting counterfeits. This achievement can be applied to automated signature recognition in large-scale exams.

關鍵字(中)

★ 離線簽名識別
★ 深度學習
★ 二階段度量學習
★ 三元組損失函數
★ 偽造簽名

關鍵字(英)

★ Offline signature recognition
★ deep learning
★ two-stage metric learning
★ triple loss function
★ forged signature

論文目次

摘要..............I
Abstract..........II
謝誌..............III
目錄..............IV
圖目錄............VII
表目錄............IX
第一章. 緒論......1
1.1 研究背景與動機....1
1.2 研究目標..........2
1.3 論文架構.....2
第二章. 文獻回顧................3
2.1 離線簽名辨識(Offline Signature Recognition)......3
2.2 簽名影像前處理............. 6
二值化(Binarization) ................... 6
骨架化(Skeletonization)................ 8
2.3 深度學習.................................... 9
二階段度量學習(Two-Stage Metric Learning) ............. 9
三元組損失函數(Triplet Loss)....... 10
第三章. 中文離線簽名辨識系統設計...... 13
3.1 系統設計方法論............. 13
3.1.1. IDEF0 階層式模組化設計....... 14
3.1.2. GRAFCET 離散事件建模......... 15
3.2 中文離線簽名辨識階層式模組架構..... 18
3.2.1. 簽名資料前處理.......... 18
3.2.2. 簽名模型訓練......... 19
3.2.3. 簽名資料辨識.................. 20
3.3 中文離線簽名辨識離散事件模型........ 21
3.3.1. 簽名資料前處理離散事件模型.......... 22
3.3.2. 模型訓練離散事件模型...... 22
3.3.3. 簽名資料辨識離散事件模型..... 24
3.4 中文離線簽名辨識高階軟體合成...... 24
3.4.1. 資料前處理狀態轉移....... 24
3.4.2. 模型訓練狀態轉移......... 25
3.4.3. 簽名辨識狀態轉移........ 27
第四章. 實驗結果............ 28
4.1. 實驗環境介紹....... 28
實驗資料庫...... 28
4.2. 模型訓練....... 30
資料前處理.......... 30
第一階段度量學習...... 32
第二階段度量學習.......... 33
性能評估方法........... 34
4.3. 實驗結果.................. 36
與孿生神經網路比較............. 39
第五章. 結論與未來展望.............. 42
5.1. 結論...................... 42
5.2. 未來方向.............. 43
參考文獻............ 44

參考文獻

[1] Kekre, H. B., & Bharadi, V. A, “Off-Line Signature Recognition Systems,” International Journal of Computer Applications, no. 27, pp. 61-70, 2010.
[2] U. Pal et al., “Signature-based biometric authentication,” in Computational Intelligence in Digital Forensics: Forensic Investigation and Applications, pp. 285-314, 2014.
[3] K. S. Radhika, S. Gopika, “Online and Offline Signature Verification: A Combined Approach,” Procedia Computer Science, vol. 46, pp. 1593-1600, 2015.
[4] M. Saleem and B. Kovari, “Online signature verification using signature down-sampling and signer-dependent sampling frequency,” Neural Computing and Applications, pp. 1-13, 2021.
[5] Y. Zhou, J. Zheng, H. Hu, and Y. Wang, “Handwritten signature verification method based on improved combined features,” Applied Sciences, vol. 11, no. 13: 5867, 2021.
[6] Lu, J.; Qi, H.; Wu, X.; Zhang, C.; Tang, Q, “Research on Authentic Signature Identification Method Integrating Dynamic and Static Features,” Applied Sciences, vol. 12, no. 19: 9904, 2022.
[7] M. R. Pourshahabi, M. H. Sigari and H. R. Pourreza, “Offline Handwritten Signature Identification and Verification Using Contourlet Transform,“ International conference of soft computing and pattern recognition,Malacca, Malaysia, pp. 670-673, 2009.
[8] M.S. Shirdhonkar and M. Corker, “Offline Handwritten Signature Identification and Verification Using Multi-Resolution Gabor Wavelet”, International Journal of Biometrics and Bioinformatics (IJBB), vol. 5, no. 4, pp. 234-248, 201
[9] Zhang, B, “Offline signature verification and identification by hybrid features and Support Vector Machine,“ International Journal of Artificial Intelligence and Soft Computing, vol. 2, no. 4, pp. 302-320, 2011.
[10] Karouni, A., Daya, B., & Bahlak, S, “Offline signature recognition using neural networks approach,“ Procedia Computer Science, vol. 3, pp. 155-161, 2011.
[11] Sam S. M., Kamardin K., Sjarif N. N. A. and Mohamed N, “Offline signature verification using deep learning convolutional neural network (CNN) architectures GoogLeNet inception-v1 and inception-v3,“ Procedia Computer Science, vol. 161, pp. 475-483, 2019.
[12] Xiao, W.; Ding, Y, “A Two-Stage Siamese Network Model for Offline Handwritten Signature Verification,“ Symmetry, vol. 14, no. 6, p. 1216, 2022.
[13] T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-987, 2002.
[14] Abu-Ain, W., Abdullah, S.N., Bataineh, B., Abu-Ain, T., & Omar, K.B, “Skeletonization Algorithm for Binary Images,“ Procedia Technology, vol. 11, pp. 704-709, 2013.
[15] Rakesh, G., & Rajpreet, K, “Skeletonization algorithm for numeral patterns,“ International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 1, no. 1, pp.63-72, 2008.
[16] Chattopadhyay, S., Manna, S., Bhattacharya, S., & Pal, U, “SURDS: Self-Supervised Attentionguided Reconstruction and Dual Triplet Loss for Writer Independent Offline Signature
Verification,“ 2022 26th International Conference on Pattern Recognition (ICPR), pp. 1600-1606,2022.
[17] L. G. Hafemann, R. Sabourin and L. S. Oliveira, “Offline handwritten signature verification - Literature review,“ 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 1-8, 2017.
[18] L. G. Hafemann, R. Sabourin and L. S. Oliveira, “Learning features for offline handwritten signature verification using deep convolutional neural networks,“ Pattern Recognition, vol. 70, pp. 163-176, 2017.
[19] Wen-ming, Z., Shao-fa, L., & Xian-gui, Z, “A hybrid scheme for off-line Chinese signature
verification,“ IEEE Conference on Cybernetics and Intelligent Systems, vol. 2, pp. 1402-1405, 2004.
[20] M. I. Malik, M. Liwicki, A. Dengel, “Evaluation of local and global features for offline signature
verification,” In Proceedings of the 1st International Workshop on Automated Forensic Handwriting
Analysis (AFHA), pp. 26-30, 2011.
[21] Wei, C., S. Lichun, et al., “Improved Zhang-Suen thinning algorithm in binary line drawing
applications,“ 2012 International Conference on Systems and Informatics (ICSAI2012), pp. 1947-1950, 2012.
[22] Németh, G., & Palágyi, K, “Topology preserving parallel thinning algorithms,“ International
Journal of Imaging Systems and Technology, vol. 21, pp. 37-41, 2011.
[23] Blum, Harry, “Biological shape and visual science (part I),” Journal of Theoretical Biology, vol.
38, pp. 205-287, 1973.
[24] Zhang, T. Y.; Suen, C. Y, “A fast parallel algorithm for thinning digital patterns,“ Communications of the ACM, vol. 27, no. 3, pp. 236-239, 1984.
[25] Guo, Z, and Hall, R W, “Parallel thinning with two-subiteration algorithms,“ Communications of the ACM, vol. 32, no. 3, pp. 359-373, 1989.
[26] Xing, E. P.; Ng, A. Y.; Jordan, M. I. & Russell, S. J, “Distance Metric Learning with Application to Clustering with Side-Information,“ Advances in Neural Information Processing Systems, vol. 15, pp. 505-512, 2002.
[27] Chopra, S., Hadsell, R., & LeCun, Y, “Learning a similarity metric discriminatively, with application to face verification,“ 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR 2005), vol. 1, pp. 539-546, 2005.
[28] Wang, Jun et al. “Two-Stage Metric Learning,” Proceedings of the 31st International Conference on International Conference on Machine Learning, vol. 32, pp. 370-378, 2014.
[29] Z. Liu, P. Luo, X. Wang and X. Tang, “Deep Learning Face Attributes in the Wild,“ IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, pp. 3730-3738, 2014.
[30] F. Schroff, D. Kalenichenko and J. Philbin, “FaceNet: A unified embedding for face recognition and clustering,“ IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp. 815-823, 2015.
[31] C.-H. Chen, M.-Y. Lin, and X.-C. Guo, “High-level modeling and synthesis of smart sensor networks for Industrial Internet of Things,” Comput. Electr. Eng, vol. 61, pp. 48-66, Jul. 2017.
[32] K. Yan et al, “Signature Detection, Restoration, and Verification: A Novel Chinese Document Signature Forgery Detection Benchmark,“ 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) , New Orleans, LA, USA, pp. 5159-5168, 2022.
[33] Andrew P. Bradley, “The use of the area under the ROC curve in the evaluation of machine learning algorithms,“ Pattern Recognition, vol. 30, pp. 1145-1159, July, 1997.
[34] Saito T, Rehmsmeier M, “The precision-recall plot is more informative than the ROC plot when
evaluating binary classifiers on imbalanced datasets,“ PLoS One, vol. 10, no. 3, Mar, 2015

指導教授

陳慶瀚(Ching-Han Chen)

審核日期

2023-6-27

推文