Offline Signature Identification Using Deep Learning and Euclidean Distance
on
Authors:
Made Prastha Nugraha, Adi Nurhadiyatna, Dewa Made Sri Arsa
Abstract:
“Hand signature is one of human characteristic that human have since birth, which can be used as identity recognition. A high accuracy signature recognition is needed to identify the right owner of signature. This study present signature identification using a combination method between Deep Learning and Euclidean Distance. 3 different signature datasets are used in this study which consist of SigComp2009, SigComp2011, and private dataset. Signature images preprocessed using binary image conversion, Region of Interest, and thinning. Several testing scenarios is applied to measure proposed method robustness, such as usage of various Pretrained Deep Learning, dataset augmentation, and dataset split ratio modifier. The best accuracy achieved is 99.44% with high precision rate.”
Keywords
Keyword Not Available
Downloads:
Download data is not yet available.
References
- [1] H. Saikia and K. Chandra Sarma, “Approaches and Issues in Offline Signature Verification System,” International Journal of Computer Applications, vol. 42, no. 16, pp. 45–52, Mar. 2012, doi: 10.5120/5780-8035.
- [2] M. Taskiran and Z. G. Cam, “Offline signature identification via HOG features and artificial neural networks,” in 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI), Jan. 2017, pp. 000083–000086, doi: 10.1109/SAMI.2017.7880280.
- [3] M. A. Djoudjai, Y. Chibani, and N. Abbas, “Offline signature identification using the histogram of symbolic representation,” 2017 5th International Conference on Electrical Engineering - Boumerdes (ICEE-B), vol. 2017-Janua, pp. 1–6, 2017, doi: 10.1109/ICEE-B.2017.8192092.
- [4] T. Sultan Rana, H. Muhammad Usman, and S. Naseer, “Static Handwritten Signature Verification Using Convolution Neural Network,” 3rd International Conference on Innovative Computing (ICIC), no. Icic, 2019, doi: 10.1109/ICIC48496.2019.8966696.
- [5] M. Thenuwara and H. R. K. Nagahamulla, “Offline handwritten signature verification system using random forest classifier,” 17th International Conference on Advances in ICT for Emerging Regions (ICTer) 2017, vol. 2018-Janua, pp. 191–196, 2017, doi: 10.1109/ICTER.2017.8257828.
- [6] E. Utami and R. Wulanningrum, “Use of Principal Component Analysis and Euclidean Distance to Identify Signature Image,” Iptek-Kom, vol. 16, no. 1, pp. 1–16, 2014, [Online]. Available: https://jurnal.kominfo.go.id/index.php/iptekkom/article/viewFile/505/327.
- [7] G. D. Angel and R. Wulanningrum, “Machine Learning untuk Identifikasi Tanda Tangan Menggunakan GLCM dan Euclidean Distance,” Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), pp. 297–301, 2020.
- [8] V. L. Blankers, C. E. Van Den Heuvel, K. Y. Franke, and L. G. Vuurpijl, “The ICDAR 2009 signature verification competition,” Proceeding 10th International Conference on Document Analysis and Recognition, ICDAR, pp. 1403–1407, 2009, doi: 10.1109/ICDAR.2009.216.
- [9] M. Liwicki et al., “Signature verification competition for online and offline skilled forgeries (SigComp2011),” Proceeding International Conference on Document Analysis and Recognition, ICDAR, pp. 1480–1484, 2011, doi: 10.1109/ICDAR.2011.294.
- [10] X. Yan, L. Wen, L. Gao, and M. Perez-Cisneros, “A Fast and Effective Image Preprocessing Method for Hot Round Steel Surface,” Mathematical Problems in Engineering, vol. 2019, 2019, doi: 10.1155/2019/9457826.
- [11] A. H. Pratomo, W. Kaswidjanti, and S. Mu’arifah, “Implementasi Algoritma Region of Interest ( ROI ) Untuk Meningkatkan Performa Algoritma Deteksi Dan Klasifikasi Kendaraan,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 7, no. 1, pp. 155–162, 2020, doi: 10.25126/jtiik.202071718.
- [12] Abhisek and K. Lakshmesha, “Thinning approach in digital image processing,” Last Accessed April, pp. 326–330, 2018.
- [13] A. Foroozandeh, A. Askari Hemmat, and H. Rabbani, “Offline Handwritten Signature Verification and Recognition Based on Deep Transfer Learning,” International Conference on Machine Vision and Image Processing. MVIP, vol. 2020-Janua, 2020, doi: 10.1109/MVIP49855.2020.9187481.
- [14] I. M. Mika Parwita and D. Siahaan, “Classification of Mobile Application Reviews using Word Embedding and Convolutional Neural Network,” Lontar Komputer Jurnal Ilmiah Teknologi Informasi, vol. 10, no. 1, p. 1, 2019, doi: 10.24843/lkjiti.2019.v10.i01.p01.
- [15] J. A. Gliner, G. A. Morgan, N. L. Leech, J. A. Gliner, and G. A. Morgan, “Measurement Reliability and Validity,” Research Methods in Applied Settings, pp. 319–338, 2021, doi: 10.4324/9781410605337-29.
- [16] S.-H. Tsang, “No Title,” Review: Xception - With Depthwise Separabale Convolution, Better THan Inception-V3, 2018. review: Xception - With Depthwise Separabale Convolution, Better THan Inception-V3 (accessed May 18, 2021).
- [17] O. Sudana, I. W. Gunaya, and I. K. G. D. Putra, “Handwriting identification using deep convolutional neural network method,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 18, no. 4, pp. 1934–1941, 2020, doi: 10.12928/TELKOMNIKA.V18I4.14864.
- [18] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proceeding IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2016-Decem, pp. 770–778, 2016, doi: 10.1109/CVPR.2016.90.
- [19] Y. Harjoseputro, I. P. Yuda, and K. P. Danukusumo, “MobileNets: Efficient Convolutional Neural Network for Identification of Protected Birds,” International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 6, pp. 2290–2296, 2020, doi: 10.18517/ijaseit.10.6.10948.
- [20] G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” Proceeding - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 2261–2269, 2017, doi: 10.1109/CVPR.2017.243.
- [21] Y. Adiwinata, A. Sasaoka, I. P. Agung Bayupati, and O. Sudana, “Fish Species Recognition with Faster R-CNN Inception-v2 using QUT FISH Dataset,” Lontar Komputer Jurnal Ilmiah Teknologi Informasi, vol. 11, no. 3, p. 144, 2020, doi: 10.24843/lkjiti.2020.v11.i03.p03.
PDF:
https://jurnal.harianregional.com/lontar/full-73362
Published
2021-08-16
How To Cite
NUGRAHA, Made Prastha; NURHADIYATNA, Adi; ARSA, Dewa Made Sri. Offline Signature Identification Using Deep Learning and Euclidean Distance.Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], v. 12, n. 2, p. 102-111, aug. 2021. ISSN 2541-5832. Available at: https://jurnal.harianregional.com/lontar/id-73362. Date accessed: 08 Jul. 2024. doi:https://doi.org/10.24843/LKJITI.2021.v12.i02.p04.
Citation Format
ABNT, APA, BibTeX, CBE, EndNote - EndNote format (Macintosh & Windows), MLA, ProCite - RIS format (Macintosh & Windows), RefWorks, Reference Manager - RIS format (Windows only), Turabian
Issue
Vol 12 No 2 (2021): Vol. 12, No. 02 August 2021
Section
Articles
Copyright
This work is licensed under a Creative Commons Attribution 4.0 International License
Discussion and feedback