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Reseach Article

Writer-independent Offline Handwritten Signature Verification using Novel Feature Extraction Techniques

by Md. Aminur Rahman, Sarker Miraz Mahfuz, S. M. Abdullah Al-Mamun
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 14
Year of Publication: 2019
Authors: Md. Aminur Rahman, Sarker Miraz Mahfuz, S. M. Abdullah Al-Mamun
10.5120/ijca2019919537

Md. Aminur Rahman, Sarker Miraz Mahfuz, S. M. Abdullah Al-Mamun . Writer-independent Offline Handwritten Signature Verification using Novel Feature Extraction Techniques. International Journal of Computer Applications. 177, 14 ( Oct 2019), 21-27. DOI=10.5120/ijca2019919537

@article{ 10.5120/ijca2019919537,
author = { Md. Aminur Rahman, Sarker Miraz Mahfuz, S. M. Abdullah Al-Mamun },
title = { Writer-independent Offline Handwritten Signature Verification using Novel Feature Extraction Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2019 },
volume = { 177 },
number = { 14 },
month = { Oct },
year = { 2019 },
issn = { 0975-8887 },
pages = { 21-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number14/30966-2019919537/ },
doi = { 10.5120/ijca2019919537 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:45:52.147066+05:30
%A Md. Aminur Rahman
%A Sarker Miraz Mahfuz
%A S. M. Abdullah Al-Mamun
%T Writer-independent Offline Handwritten Signature Verification using Novel Feature Extraction Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 14
%P 21-27
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Signature is critical for authentication and authorization in commercial, financial and legal transactions and fittingly, it is one of the most commonly used biometrics for authentication. Hence, an accurate and efficient signature verification system is required. The objective of signature verification is to discriminate the original signatures from the forged ones. It is a challenging task as even two signatures of the same person possess variations in different areas such as the starting and ending positions, the angle of inclination, relative spacing between letters, height, width etc. Offline signature verification is even more challenging as it is devoid of the dynamic information about the signing process. Although numerous research works have been done in the area of offline signature verification in last decades, it still remains an open research problem. There are three common phases in signature verification system: image preprocessing, feature extraction and verification. In this paper, two novel features have been presented that can be extracted from preprocessed signature images in the feature extraction phase. The proposed features are: i) Stroke angle and average intersected points ii) Pixel density of the signature nucleus. The goal of this research is to strengthen the feature set with the proposed features what will help to get more accurate verification of the signatures.

References
  1. Debnath Bhattacharyya, Rahul Tanjan, Farkhod Aliserov A, and Minkyu Choi, “Biometric Authentication: A Review”, International Journal of u and e - Service, Science and Technology, 2009.
  2. Siti Norul Huda Sheikh Abdullah and Khairuddin Omar, “State-of-the-Art in Offline Signature Verification System”, International Conference on Pattern Analysis and Intelligent Robotics (ICPAIR)-2011 IEEE.
  3. D. Bertolini, L. S. Oliveira, E. Justino, and R. Sabourin. “Reducing forgeries in writer-independent off-line signature verification through ensemble of classifiers”. Pattern Recognition, 43(1), January 2010.
  4. Henali P., Shivani D., Pooja D and Abha D. “Review on offline signature recognition and verification techniques”. International Journal of Computer Applications (IJCA), June 2018.
  5. Juan Hu and Youbin Chen. “Offline Signature Verification Using Real Adaboost Classifier Combination of Pseudo-dynamic Features”. Document Analysis and Recognition, 12th International Conference on, pages 1345–1349, August 2013.
  6. Luiz G. Hafemann, Robert Sabourin, and Luiz S. Oliveira. “Writer independent feature learning for Offline Signature Verification using Deep Convolutional Neural Networks”. International Joint Conference on Neural Networks, pages 2576–2583, July 2016.
  7. M. Pourshahabi, M. Hoseyn Sigari, and H. Pourreza. “Offline handwritten signature identification and verification using contourlet transform”. Soft Computing and Pattern Recognition, Int. conference of. IEEE, 2009.
  8. J. Ruiz-del Solar, C. Devia, P. Loncomilla, and F. Concha. “Offline Signature Verification Using Local Interest Points and Descriptors”. Progress in Pattern Recognition, Image Analysis and Applications, number 5197. Springer, 2008.
  9. J. F. Vargas, M. A. Ferrer, C. M. Travieso, and J. B. Alonso. “Offline signature verification based on grey level information using texture features”. Pattern Recognition, 44(2):375–385, February 2011.
  10. R. Zouari, R. Mokni, and M. Kherallah. “Identification and verification system of offline handwritten signature using fractal approach”. Image Processing, Applications and Systems Conference (IPAS), 2014 First International, pages 1–4, November 2014.
  11. L. Ravi Kumar and A.Sudhir Babu, “Genuine and Forged Offline Signature Verification Using Back Propagation Neural Networks”, (IJCSIT) International Journal of Computer Science and Information Technologies, 2011.
  12. B H Shekar and R.K.Bharathi, “Eigen-signature: A Robust and an Efficient Offline Signature Verification Algorithm” IEEE-International Conference on Recent Trends in Information Technology, ICRTI- 2011 IEEE.
  13. Miguel A. Ferrer, Francisco Vargas, Carlos M. Travieso and Jesus B. Alonso, “Signature Verification using Local Directional Pattern (LDP)” International Conference on computer security technology (ICCST)-2010.
  14. Meenakshi K. Kalera, Sargur Srihari, and Aihua Xu. “Offline signature verification and identification using distance statistics”. International Journal of Pattern Recogn., 18(07):1339–1360, November 2004.
  15. S.T. Kolhe, S. E. Pawar. “Offline Signature Verification Using Neural Network”, International Journal of Modern Engineering Research (IJMER), Vol.2, Issue.3, May-June 2012.
  16. Ashwini Pansare, Shalini Bhatia “Off-line Signature Verification Using Neural Network”, International Journal of Scientific & Engineering Research, Volume 3, Issue 2, February-2012.
  17. Paigwar Shikha, Shukla Shailja, “Neural Network Based Offline Signature Recognition and Verification System”, Research Journal of Engineering Sciences ISSN 2278 – 9472, Vol. 2(2), 11-15, February 2013.
  18. P. Fishwick, “Neural network models in simulation: A comparison with traditional modeling approaches,” Working Paper, University of Florida, Gainesville, FL, 1989.
  19. Peter Shaohua Deng, Hong-Yuan Mark Liao, Chin Wen Ho, and Hsiao-Rong Tyan. “Wavelet-Based Off-Line Handwritten Signature Verification”. Computer Vision and Image Understanding, 76(3), December 1999.
  20. A. El-Yacoubi, E. J. R. Justino, R. Sabourin, and F. Bortolozzi. “Offline signature verification using HMMs and cross-validation”. Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop, volume 2. IEEE, 2000.
  21. Dr. S. Adebayo Daramola and Prof. T. Samuel Ibiyemi, “Offline Signature Recognition using Hidden Markov Model (HMM)”, International Journal of Computer Applications 10(2):17-22, November 2010.
  22. S. Ghandali and M.E. Moghaddam. “A Method for Off-line Persian Signature Identification and Verification Using DWT and Image Fusion”. IEEE International Symposium on Signal Processing and Information Technology, ISSPIT, pages 315–319, December 2008.
  23. Daramola Samuel, Ibiyemi Samuel, “Novel Feature Extraction Technique for Off-line Signature Verification System”, International Journal of Engineering Science and Technology, Vol. 2(7), 2010, 3137-3143.
Index Terms

Computer Science
Information Sciences

Keywords

Offline Signature Verification Biometric Authentication Forgery Detection Neural Network Novel Features.