CFP last date
22 April 2024
Reseach Article

An Efficient Offline Signature Verification System using Local Features

by Basheer Mohamad Al-Maqaleh, Abdulbaset Mohammed Qaid Musleh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 131 - Number 10
Year of Publication: 2015
Authors: Basheer Mohamad Al-Maqaleh, Abdulbaset Mohammed Qaid Musleh
10.5120/ijca2015907444

Basheer Mohamad Al-Maqaleh, Abdulbaset Mohammed Qaid Musleh . An Efficient Offline Signature Verification System using Local Features. International Journal of Computer Applications. 131, 10 ( December 2015), 39-44. DOI=10.5120/ijca2015907444

@article{ 10.5120/ijca2015907444,
author = { Basheer Mohamad Al-Maqaleh, Abdulbaset Mohammed Qaid Musleh },
title = { An Efficient Offline Signature Verification System using Local Features },
journal = { International Journal of Computer Applications },
issue_date = { December 2015 },
volume = { 131 },
number = { 10 },
month = { December },
year = { 2015 },
issn = { 0975-8887 },
pages = { 39-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume131/number10/23489-2015907444/ },
doi = { 10.5120/ijca2015907444 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:26:59.498946+05:30
%A Basheer Mohamad Al-Maqaleh
%A Abdulbaset Mohammed Qaid Musleh
%T An Efficient Offline Signature Verification System using Local Features
%J International Journal of Computer Applications
%@ 0975-8887
%V 131
%N 10
%P 39-44
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The most common secure personal authentication in biometrics is handwritten signature. It’s widely used in many felids as banks , business transactions , and documents which are being authorized via signatures. The main challenging problem in design offline signature verification system is the phase of extracting features that distinguish between forged and genuine signatures. In this paper, a novel feature of extraction method based on static image splitting is proposed. The center of density of the signature image is used for the splitting. In the proposed system, a new feature called Pixel Length (F4)is suggested. This feature is used in combination with other three features: Pixel Density (F1), Cell Angle (F2), and Pixel Angle (F3) which are common features in the offline verification signature process. Euclidean distance measure was used for classification. The proposed system is implemented and tested using GPDS database. The performance of the proposed system is measured and the experimental results show the usefulness and effectiveness of the proposed system.

References
  1. Guerbai, Y., Chibani, Y. and Abbas, N. 2012. One-class versus bi-class SVM classifier for offline signature verification. In Multimedia Computing and Systems, International Conference on IEEE (ICMCS2012),  pp. 206-210.‏
  2. Eliza Y. D. 2013. Biometric From Fictions to Practice . U.S. Government works Version , International Standard Book no.13, pp. 978-981.
  3. Vitthal , K. B. and Anil, R. K. 2013. Automatic static signature verification system. International Journal of Computational Engineering Research, vol.3,Issue.2, pp. 8-12.
  4. Bhattacharya, I., Ghosh, P. and Biswas, S. 2013. Offline signature verification using pixel matching technique. Procedia Technology.  vol. 10, pp. 970-977.‏
  5. Bertolini, D., Oliveira, L. S., Justino, E. and Sabourin, R. 2010. Reducing forgeries in writer-independent offline signature verification through ensemble of classifiers. Pattern Recognition. vol. 43, pp. 387-396.‏
  6. Batista, L., Granger, E. and Sabourin, R. 2012. Dynamic selection of generative–discriminative ensembles for offline signature verification. Pattern Recognition, vol. 45 , pp.1326-1340.‏
  7. Guerbai, Y., Chibani, Y. and Hadjadji, B. 2015. The effective use of the one-class SVM classifier for handwritten signature verification based on writer-independent parameters. Pattern Recognition, vol. 48, pp. 103-113.‏
  8. Huang K., and Hong Y. 1997. Offline signature verification based on geometric feature extraction and neural network classification. Pattern Recognition, vol. 30 , pp. 9-17.
  9. Ghandali, S. and Moghaddam, M. E. 2009. Offline persian signature identification and verification based on image registration and fusion. Journal of Multimedia,  vol. 4, pp.137-144.‏
  10. Baltzakis, H., and Papamarkos, N. 2001. A new signature verification technique based on a two-stage neural network classifier. Engineering Applications of Artificial intelligence, vol. 14, pp.95-103.‏
  11. Srivastava, S. and Agarwal, S. 2013. Offline signature verification based on pixel oriented and component oriented feature extraction. International Journal of Advanced Research in Computer Science, vol. 4, pp.77-83.‏
  12. Abuhaiba, I. S. 2007. Offline signature verification using graph matching. Turk J Elec Engin, vol.15, pp. 89-104.
  13. Al-Omari, Y. M., Siti Norul H. S. Abdullah and Omar, K. 2011. State-of-the-art in offline signature verification system. In Pattern Analysis and Intelligent Robotics (ICPAIR2011), International Conference on IEEE , vol. 1, pp. 59-64.‏
  14. Samuel, D. and Samuel, I. 2010. Novel feature extraction technique for offline signature verification system. International Journal of Engineering Science and Technology, vol. 2, pp. 3137-3143.‏
  15. Pansare, A. and Bhatia, S.2012. Handwritten signature verification using neural network. International Journal of Applied Information Systems (IJAIS), vol. 1, no. 2, pp.44-49.
  16. Ismail, M. A. and Gad, S. 2000. Offline arabic signature recognition and verification. Pattern Recognition,  vol. 33, pp.1727-1740.‏
  17. Ahmed, S. M. 2012. Offline arabic signature verification using geometrical features. National Workshop on Information Assurance Research, Proceedings of (WIAR2012) ,pp. 1-6.‏
  18. Nguyen, V., Blumenstein, M., Muthukkumarasamy, V., and Leedham, G. 2007. Offline signature verification using enhanced modified direction features in conjunction with neural classifiers and support vector machines, In Proceedings of 9th International Conference on Document Analysis and Recognition ( ICDAR 2007), IEEE Computer Society Washington, USA, vol. 2, pp. 734-738.‏
  19. Srihari, S. N., Xu, A. and Kalera, M. K. 2004. Learning strategies and classification methods for offline signature verification. In Proceedings of 9th International Workshop on Frontiers in Handwriting Recognition (IWFHR-2004), IEEE Computer Society Washington, DC, USA , pp. 161-166.
  20. Ferrer, M., Vargas, J., Morales, A. and Ordóñez, A. 2012. Robustness of offline signature verification based on gray level features. IEEE Transactions on Information Forensics and Security, vol. 7, pp. 966-977.‏
  21. Tan, X., Jaafar, A. A., Yahya, A., Ahmad, R., Zain, A., Salman, M. and Linlin, W. 2013. Offline signature verification system based on DWT and common features extraction. Journal of Theoretical and Applied Information Technology, vol. 51, pp. 165-174‏.
  22. Hetal V. Davda. and S. K. G. 2014. Offline signature verification system using energy on grid level , International Journal of Engineering Research, vol. 3 , pp. 104-107.
  23. Htight ,W. H. and Soe, A.L. 2014. Offline signature verification system using neural network, International Conference on Advances in Engineering and Technology, vol. 1, pp.302-306.
  24. Sabourin, R., Genest, G. and Prêteux, F. J. 1997. Offline signature verification by local granulometric size distributions. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19,Issue 9, pp. 976-988.‏
  25. Majhi, B., Reddy, Y. S. and Babu, D. P. 2006. Novel features for offline signature verification. International Journal of Computers, Communications & Control, vol.1, pp. 17-24.‏
  26. Ramachandra, A. C., Rao, J. S., Raja, K. B., Venugopla, K. R., and Patnaik, L. M. 2009. Robust offline signature verification based on global features. In Advance Computing Conference, IEEE International (IACC 2009), pp. 1173-1178.
  27. Vargas, J. F., Ferrer, M., Travieso, C. M. and Alonso, J. B. 2007. Offline handwritten signature GPDS-960 corpus, International Conference on Document Analysis and Recognition (ICDAR), vol. 2, pp. 764-768.
  28. Ferrer, M., Díaz-Cabrera, M. and Morales, A. 2013. Synthetic offline signature image generation. In Proceedings of the 9th International Conference on Biometrics (ICB2013) , IEEE , pp. 1-7.‏
  29. R. Verma and Rao, D.S. 2013. Offline signature verification and identification using angle feature and pixel density feature and both method together. International Journal of Soft Computing and Engineering (IJSCE), vol. 2, Issue 4, pp.740-746.
  30. Kumar, R., Sharma, J. D. and Chanda, B. 2012. Writer independent offline signature verification using surroundedness feature. Pattern Recognition Letters, vol.33, pp. 301-308.
Index Terms

Computer Science
Information Sciences

Keywords

Biometrics Offline Signature Verification Feature Extraction Euclidean Distance Model.