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Offline Signature Verification based on Ensemble of Features using Support Vector Machine

by Sunil Kumar D.S.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 45
Year of Publication: 2023
Authors: Sunil Kumar D.S.
10.5120/ijca2023922547

Sunil Kumar D.S. . Offline Signature Verification based on Ensemble of Features using Support Vector Machine. International Journal of Computer Applications. 184, 45 ( Feb 2023), 24-29. DOI=10.5120/ijca2023922547

@article{ 10.5120/ijca2023922547,
author = { Sunil Kumar D.S. },
title = { Offline Signature Verification based on Ensemble of Features using Support Vector Machine },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2023 },
volume = { 184 },
number = { 45 },
month = { Feb },
year = { 2023 },
issn = { 0975-8887 },
pages = { 24-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number45/32607-2023922547/ },
doi = { 10.5120/ijca2023922547 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:24:56.465486+05:30
%A Sunil Kumar D.S.
%T Offline Signature Verification based on Ensemble of Features using Support Vector Machine
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 45
%P 24-29
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we propose a novel feature representation technique namely Edge Histogram and 4 Directional Histogram for offline signature verification system. Edge is a curve or point where the intensity of an image changes rapidly. Edges represent the boundary of object of an image. Edge detection is a process of detecting edges of an image. Several algorithms are available to detect edges effectively from an image. Canny, Roberts, Prewitt and Sobel are several popular available edge detectors. In our approach we used Sobel operator for edge detection. We also applied radon transform on signature samples and obtained fractal properties with the help of box-counting method. Finally fusion of these features forms a feature vector. We employed Support vector machine for classification. Experiments are conducted on bench mark dataset namely CEDAR and GPDS. The obtained experimental results exhibit the performance of the proposed method.

References
  1. Calik.N., Kurban, O.C., Yilmaz,A.R., Yild irim, T., & Ata, L.D. (2019). Large–Scale offline signature recognition via deep neural networks and feature embedding. Neurocomputing 359, 1-4.
  2. Hadjadji, B., Chibani ,Y., & Nemmour H.(2017). An Efficient Open System for Offline Handwritten Signature Identification based on Curvelet Transform and One-Class Principal Component Analysis. Neurocomputing, 265, 66-77
  3. Rahmi,A. Wijayaningrum , V.N., Mahmudy,W.F., & Parewe,A.M.(2016). Offline Signature Recognition using Back Propagation Neural Network. Indonesian Journal of Electrical Engineering and Computer Science, 4(3), 678-683
  4. Ebrahim,A.Y., & Sulong, G.(2014). Offline Handwritten Signature Verification using Back Propagation Artificial Neural Network Matching Technique. Journal of Theoretical and Applied Information Technology, 65(3).
  5. Kumar,R., Kaur,S., & Gupta,B. Signature Recognition using Statistical-ANN Hybrid Technique. International Journal of Scientific Research and Engineering Development –Volume 2 Issue 2 Mar-Apr 2019.
  6. Hafemann, L. G., Sabourin, R., & Oliveira, L. S. (2017). Learning features for offline handwritten signature verification using deep convolutional neural networks. Pattern Recognition, 70, 163-176.
  7. Zois, E. N., Alewijnse, L., & Economou, G. (2016). Offline signature verification and quality characterization using poset-oriented grid features. Pattern Recognition, 54, 162-177
  8. Serdouk, Y., Nemmour, H., & Chibani, Y. (2016). New off-line handwritten signature verification method based on artificial immune recognition system. Expert Systems with Applications, 51, 186-194.
  9. Yılmaz, M. B., & Yanıkoğlu, B. (2016). Score level fusion of classifiers in off-line signature verification. Information Fusion, 32, 109-119.
  10. Radhika, K. S., & Gopika, S. (2015). Online and offline signature verification: a combined approach. Procedia Computer Science, 46, 1593-1600.
  11. Yilmaz, M. B., Yanikoglu, B., Tirkaz, C., & Kholmatov, A. (2011, October). Offline signature verification using classifier combination of HOG and LBP features. In 2011 international joint conference on Biometrics (IJCB) (pp. 1-7). IEEE.
  12. Kalera, M. K., Srihari, S., & Xu, A. (2004). Offline signature verification and identification using distance statistics. International Journal of Pattern Recognition and Artificial Intelligence, 18(07), 1339-1360.
  13. Chen, S., Srihari, S.: Use of exterior contours and shape features in off-line signature verification. In: ICDAR, pp. 1280–1284 (2005).
  14. Kumar, R., Kundu, L., Chanda, B., Sharma, J.D.: A writer-independent off-line signature verification system based on signature morphology. In: Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia IITM ’10, pp. 261–265. ACM, New York, NY, USA (2010)
  15. Shekar, B.H., Bharathi, R.K., Pilar, B.: Local morphological pattern spectrum based approach for off-line signature verification. In: International Conference on Pattern Recognition and Machine Intelligence, pp. 335–342. Springer (2013)
  16. Kumar, R., Sharma, J., Chanda, B. (2012): Writer-independent off-line signature verification using surroundedness feature. Pattern recognition letters. 33(3), 301–308
  17. Kumar, M.M., Puhan, N.: Inter-point envelope based distance moments for offline signature verification. In: 2014 International Conference on Signal Processing and Communications (SPCOM), pp. 1–6. IEEE (2014)
  18. Ferrer, M., Alonso, J. and Travieso, C., 2005. Offline geometric parameters for automatic signature verification using fixed-point arithmetic. Pattern Analysis and Machine Intelligence, IEEE Transactions on 27(6), pp. 993–997.
  19. Vargas, J., Ferrer, M., Travieso, C. and Alonso, J., 2011. Offline signature verification based on grey level information using texture features. Pattern Recognition 44(2), pp. 375 – 385.
  20. Ruiz-Del-Solar, J., Devia, C., Loncomilla, P. and Concha, F., 2008. Offline signature verification using local interest points and descriptors. In: Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications, CIARP ’08, Springer-Verlag, Berlin, Heidelberg, pp. 22–
  21. https://en.wikipedia.org/wiki/Support-vector_machine
  22. Pilar, B., Shekar, B. H., & Sunil Kumar, D. S. (2019). Multi-scale Local Binary Patterns-A Novel Feature Extraction Technique for Offline Signature Verification. In Recent Trends in Image Processing and Pattern Recognition: Second International Conference, RTIP2R 2018, Solapur, India, December 21–22, 2018, Revised Selected Papers, Part III 2 (pp. 140-148). Springer Singapore.
  23. Parameshachari, B. D., & Kumar, D. S. (2022, October). SVM Based Brain Tumor Detection and Classification System. In 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon) (pp.1-4).IEEE.
Index Terms

Computer Science
Information Sciences

Keywords

Signature Verification Support Vector Machine Classification Edge Histogram Edge Directional Histogram.

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