* Corresponding Author
Hide
Show all
Siamese-Network Based Signature Verification using Self Supervised Learning
https://doi.org/10.22146/ijccs.74627 Muhammad Fawwaz Mayda(1*), Aina Musdholifah(2)
(1) Undergraduate Program of Computer Science, FMIPA UGM, Yogyakarta
(2) Departement of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(*) Corresponding Author
Abstract
The use of signatures is often encountered in various public documents ranging from academic documents to business documents that are a sign that the existence of signatures is crucial in various administrative processes. The frequent use of signatures does not mean a procedure without loopholes, but we must remain vigilant against signature falsification carried out with various motives behind it. Therefore, in this study, a signature verification system was developed that could prevent the falsification of signatures in public documents by using digital imagery of existing signatures. This study used neural networks with siamese network-based architectures that also empower self-supervised learning techniques to improve accuracy in the realm of limited data. The final evaluation of the machine learning method used gets a maximum accuracy of 83% and this result is better than the machine learning model that does not involve self-supervised learning methods.
Keywords
Full Text:
PDFReferences
[1] N. Sharma, S. Gupta, and P. Mehta, “A Comprehensive Study on Offline Signature Verification,” in Journal of Physics: Conference Series, 2021, vol. 1969, no. 1, p. 012044, doi: 10.1088/1742-6596/1969/1/012044.
[2] S. Dey, A. Dutta, J. I. Toledo, S. K. Ghosh, J. Llados, and U. Pal, “SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification,” Jul. 2017, Accessed: Aug. 29, 2021. [Online]. Available: www.elsevier.com.
[3] N. H. Al-banhawy, H. Mohsen, and N. Ghali, “SIGNATURE IDENTIFICATION AND VERIFICATION SYSTEMS: A COMPARATIVE STUDY ON THE ONLINE AND OFFLINE TECHNIQUES,” Futur. Comput. Informatics J., vol. 5, no. 1, pp. 28–45, 2020, doi: 10.54623/fue.fcij.5.1.3.
[4] S. Jerome Gideon, A. Kandulna, A. A. Kujur, A. Diana, and K. Raimond, “Handwritten signature forgery detection using convolutional neural networks,” Procedia Comput. Sci., vol. 143, pp. 978–987, 2018, doi: 10.1016/j.procs.2018.10.336.
[5] A. Geron, Hands–On Machine Learning with Scikit–Learn and TensorFlow 2nd edition. 2019.
[6] J. Poddar, V. Parikh, and S. K. Bharti, “Offline Signature Recognition and Forgery Detection using Deep Learning,” Procedia Comput. Sci., vol. 170, no. 2019, pp. 610–617, 2020, doi: 10.1016/j.procs.2020.03.133.
[7] S. N. Srihari, H. Srinivasan, S. Chen, and M. J. Beal, “Machine Learning for Signature Verification,” in Studies in Computational Intelligence, vol. 90, 2008, pp. 387–408.
[8] E. Parcham, M. Ilbeygi, and M. Amini, “CBCapsNet: A novel writer-independent offline signature verification model using a CNN-based architecture and capsule neural networks,” Expert Syst. Appl., vol. 185, no. February, p. 115649, 2021, doi: 10.1016/j.eswa.2021.115649.
[9] R. Takahashi, T. Matsubara, and K. Uehara, “A novel weight-shared multi-stage CNN for scale robustness,” IEEE Trans. Circuits Syst. Video Technol., vol. 29, no. 4, pp. 1090–1101, 2019, doi: 10.1109/TCSVT.2018.2822773.
[10] A. Foroozandeh, A. Askari Hemmat, and H. Rabbani, “Offline Handwritten Signature Verification and Recognition Based on Deep Transfer Learning,” Iran. Conf. Mach. Vis. Image Process. MVIP, vol. 2020-Janua, 2020, doi: 10.1109/MVIP49855.2020.9187481.
[11] M. M. Hameed, R. Ahmad, M. L. M. Kiah, and G. Murtaza, “Machine learning-based offline signature verification systems: A systematic review,” Signal Process. Image Commun., vol. 93, no. October 2020, p. 116139, 2021, doi: 10.1016/j.image.2021.116139.
[12] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 770–778, doi: 10.1109/CVPR.2016.90.
[13] K. Huang, Y. Wang, M. Tao, and T. Zhao, “Why do deep residual networks generalize better than deep feedforward networks? — A neural tangent kernel perspective,” Adv. Neural Inf. Process. Syst., vol. 2020-Decem, 2020.
[14] Y. Sun, K. Fu, Z. Wang, C. Zhang, and J. Ye, “Road network metric learning for estimated time of arrival,” Proc. - Int. Conf. Pattern Recognit., pp. 1820–1827, Jun. 2020, doi: 10.1109/ICPR48806.2021.9412145.
[15] Mahmut Kaya and Hasan Sakir Bilge, “Deep Metric Learning : A Survey,” Symmetry (Basel)., vol. 11.9, p. 1066, 2019.
[16] S. Chopra, R. Hadsell, and Y. LeCun, “Learning a similarity metric discriminatively, with application to face verification,” in Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, 2005, vol. I, pp. 539–546, doi: 10.1109/CVPR.2005.202.
[17] F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015, vol. 07-12-June, pp. 815–823, doi: 10.1109/CVPR.2015.7298682.
[18] A. Hermans, L. Beyer, and B. Leibe, “In Defense of the Triplet Loss for Person Re-Identification,” 2017, [Online]. Available: http://arxiv.org/abs/1703.07737.
[19] H. Xuan, A. Stylianou, X. Liu, and R. Pless, “Hard Negative Examples are Hard, but Useful,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12359 LNCS, pp. 126–142, 2020, doi: 10.1007/978-3-030-58568-6_8.
[20] J. Bromley et al., “Signature Verification using a ‘Siamese’ Time Delay Neural Network,” 1994.
[21] L. G. Hafemann, R. Sabourin, and L. S. Oliveira, “Learning features for offline handwritten signature verification using deep convolutional neural networks,” Pattern Recognit., vol. 70, pp. 163–176, 2017, doi: 10.1016/j.patcog.2017.05.012.
[22] Jahandad, S. M. Sam, K. Kamardin, N. N. Amir Sjarif, and N. Mohamed, “Offline signature verification using deep learning convolutional Neural network (CNN) architectures GoogLeNet inception-v1 and inception-v3,” Procedia Comput. Sci., vol. 161, pp. 475–483, 2019, doi: 10.1016/j.procs.2019.11.147.
[23] T. Younesian, S. Masoudnia, R. Hosseini, and B. N. Araabi, “Active Transfer Learning for Persian Offline Signature Verification,” in 4th International Conference on Pattern Recognition and Image Analysis, IPRIA 2019, 2019, vol. 2018-Janua, pp. 234–239, doi: 10.1109/PRIA.2019.8786013.
[24] M. Kurowski, A. Sroczyński, G. Bogdanis, and A. Czyżewski, “An automated method for biometric handwritten signature authentication employing neural networks,” Electron., vol. 10, no. 4, pp. 1–19, 2021, doi: 10.3390/electronics10040456.
[25] A. Jaiswal, A. R. Babu, M. Z. Zadeh, D. Banerjee, and F. Makedon, “A Survey on Contrastive Self-Supervised Learning,” Technologies, vol. 9, no. 1, p. 2, Dec. 2020, doi: 10.3390/technologies9010002.
[26] L. Jing and Y. Tian, “Self-Supervised Visual Feature Learning with Deep Neural Networks: A Survey,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 43, no. 11, pp. 4037–4058, 2021, doi: 10.1109/TPAMI.2020.2992393.
[27] L. Weng, “Self-Supervised Representation Learning,” 2019. https://lilianweng.github.io/lil-log/2019/11/10/self-supervised-learning.html (accessed Nov. 11, 2021).
[28] T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in 37th International Conference on Machine Learning, ICML 2020, 2020, vol. PartF16814, pp. 1575–1585, Accessed: Sep. 10, 2021. [Online]. Available: https://github.com/google-research/simclr.
[29] M. K. Kalera, S. Srihari, and A. Xu, “Offline signature verification and identification using distance statistics,” in International Journal of Pattern Recognition and Artificial Intelligence, 2004, vol. 18, no. 7, pp. 1339–1360, doi: 10.1142/S0218001404003630.
[30] L. Van Der Maaten and G. Hinton, “Visualizing Data using t-SNE,” J. Mach. Learn. Res., vol. 9, pp. 2579–2605, 2008.
[31] A. Newell and J. Deng, “How useful is self-supervised pretraining for visual tasks?,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2020, pp. 7343–7352, doi: 10.1109/CVPR42600.2020.00737.
DOI: https://doi.org/10.22146/ijccs.74627
Article Metrics
Abstract views : 485
| 
views : 526
Refbacks
- There are currently no refbacks.
Copyright (c) 2023 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1