CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Handwritten Signature Verification (Offline) using Neural Network Approaches: A Comparative Study

by Tirtharaj Dash, Tanistha Nayak, Subhagata Chattopadhyay
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 57 - Number 7
Year of Publication: 2012
Authors: Tirtharaj Dash, Tanistha Nayak, Subhagata Chattopadhyay
10.5120/9128-3295

Tirtharaj Dash, Tanistha Nayak, Subhagata Chattopadhyay . Handwritten Signature Verification (Offline) using Neural Network Approaches: A Comparative Study. International Journal of Computer Applications. 57, 7 ( November 2012), 33-41. DOI=10.5120/9128-3295

@article{ 10.5120/9128-3295,
author = { Tirtharaj Dash, Tanistha Nayak, Subhagata Chattopadhyay },
title = { Handwritten Signature Verification (Offline) using Neural Network Approaches: A Comparative Study },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 57 },
number = { 7 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 33-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume57/number7/9128-3295/ },
doi = { 10.5120/9128-3295 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:59:50.006313+05:30
%A Tirtharaj Dash
%A Tanistha Nayak
%A Subhagata Chattopadhyay
%T Handwritten Signature Verification (Offline) using Neural Network Approaches: A Comparative Study
%J International Journal of Computer Applications
%@ 0975-8887
%V 57
%N 7
%P 33-41
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Forgery detection has been a challenging area in the field of biometry, e. g. , handwritten signatures. Signature verification is a bi-objective optimization problem. The two crucial parameters are accuracy and time of computation. In this work, a comprehensive study on application of Adaptive Resonance Theory (ART) Nets (Type 1 and 2) and Associative Memory Net (AMN) has been conducted. To decrease the time complexity a corresponding parallel version using OpenMP is developed for each algorithm. The algorithms are trained with the original/genuine signature and tested with a sample of twelve very similar-looking forged signatures. The study concludes that ART-1 detects fake signatures with an accuracy of 99. 89%; whereas, ART-2 and AMN detect forgery with accuracies of 99. 99% and 75. 68% respectively which are comparable to other methods cited in this paper.

References
  1. T. Dash, T. Nayak, S. Chattopadhyay, Offline Verification of Hand Written Signature Using Adaptive Resonance Theory Net (Type-1), in Proc: IEEE Int. Conf. Electronics Computer Technology (ICECT) (2012), Vol-2, pp. 205-210.
  2. X. Xiao, G. Leedham, Signature verification using a modified Bayesian network, Pattern Recognition (2002) 35, 983-995.
  3. Y. Mizukami, M. Yoshimura, H. Miike, I. Yoshimura, An off-line signature verification system using an extracted displacement function, Pattern Recognition Letters (2002), 23, 1569-1577.
  4. A. Kholmatov, B. Yanikoglu, Identity authentication using improved online signature verification method, Pattern Recognition Letters (2005), 26, 1400-2408.
  5. J. C. M. Romo, R. A. Silva, Optimal Prototype functions of features for online signature verification, International Journal of Pattern Recognition and Artificial Intelligence, Volume: 18, Issue: 7(2004) pp. 1189-1206.
  6. M. Ammar, Progress in verification of skillfully simulated handwritten signatures, International Journal of Pattern Recognition and Artificial Intelligence, Volume: 5, Issues: 1-2(1991) pp. 337-351.
  7. H. Cardot, M. Revenu, B. Victorri, M. J. Revillet, A static signature verification system based on cooperating neural networks architecture, International Journal of Pattern Recognition and Artificial Intelligence, Volume: 8, Issue: 3(1994) pp. 679-692.
  8. B. Fang, X. You, W. S. Chen, Y. Y. Tang, Matching algorithm using wavelet thinning features for offline signature verification, International Journal of Pattern Recognition and Artificial Intelligence, Volume: 5, Issue: 1(2007) pp. 27-38.
  9. B. Fang, Y. Y. Wang, C. H. Leung, K. W. Tse, Y. Y. Tang, P. C. K. Kwok, Y. K. Wong, Offline signature verification by the analysis of cursive strokes, International Journal of Pattern Recognition and Artificial Intelligence, Volume: 15, Issue: 4(2001) pp. 659-673.
  10. S. Inglis, I. H. Witten, Compression-based Template Matching, in proc: IEEE Data Compression Conference (1994), pp. 106-115.
  11. M. K. Kalera, S. Srihari, A. Xu, Offline signature verification and identification using distance statistics, International Journal of Pattern Recognition and Artificial Intelligence, Volume: 18, 7(2004) pp. 1339-1360.
  12. N. S. Kamel, S. Sayeed, SVD-based signature verification technique using data glove, International Journal of Pattern Recognition and Artificial Intelligence, Volume: 22, 3(2008) pp. 431-443.
  13. J. Wen, B. Fang, Y. Y. Tang, P. S. P. Wang, M. Cheng, T. Zhang, Combining EODH and Directional Gradient Density for offline signature verification, International Journal of Pattern Recognition and Artificial Intelligence, Volume: 23, 6(2009) pp. 1161-1177.
  14. J. Bromley, J. W. Bentz, L. Bottou, I. Guyon, Y. Lecun, C. Moore, E. Sackinger, R. Shah, Signature Verificaiton using "SIAMESE" Time Delay neural network, International Journal of Pattern Recognition and Artificial Intelligence, Volume: 7, 4(1993) pp. 669-688.
  15. I. Yoshimura, M. Yoshimura, off-line signature of Japanese signatures after elimination of background patters, International Journal of Pattern Recognition and Artificial Intelligence, Volume: 8, 3(1994) pp. 693-708.
  16. W. Nelson, W. Turin, T. Hastie, Statistical method for on-line signature verification, International Journal of Pattern Recognition and Artificial Intelligence, Volume: 8, 3(1994) pp. 749-773.
  17. B. Li, D. Zhang, K. Wang, Online signature verification by combining shape contexts and local features, International Journal of Pattern Recognition and Artificial Intelligence, Volume: 6, 3(2006) pp. 407-420.
  18. M. S. Akshoy, H. Mathkour, Signature verification using rules 3-ext inductive learning system, International Journal of Physical Science, Vol. 6(18) (2011), pp. 4428-4434.
  19. S. Patil, S. Dewangan, Neural Network-based offline handwritten signature verification using Hu's moment invariant analysis, International Journal of Engineering and Advanced Technology, Vol. 1(1) (2011), pp. 73-79.
  20. L. Y. Tseng, T. H. Huang, An online Chinese signature verification scheme based on the ART1 neural network, in proc: Int. Conf. Neural Networks, vol. 3 (1992), pp. 624-630.
  21. P. Mautner, O. Rohlik, V. Matousek, J. Kempf, Signature verification using ART-2 neural network, in proc: 9th Int. Conf. Neural Information Processing, vol. 2 (2002), pp. 636- 639.
  22. T. Dash, S. Chattopadhyay, T. Nayak, "Handwritten Signature Verification using Adaptive Resonance Theory Type-2 (ART-2) Net". Journal of Global Research in Computer Science (2012) vol. 3 issue 8, pp. 21-25.
  23. T. Dash, T. Nayak, S. Chattopadhyay, "Offline Handwritten Signature Verification using Associative Memory Net". International Journal of Advanced Research in Computer Engineering & Technology (2012), vol. 1 issue 4, pp. 370-374.
  24. R. Chandra, L. Dagum, D. Kohr, D. Maydan, J. McDonald, R. Menon, Parallel programming in OpenMP, Morgan Kaufmann Publisher (2001). ISBN 1-55860-671-8.
Index Terms

Computer Science
Information Sciences

Keywords

Forgery detection signature verification bi-objective optimization Adaptive Resonance Theory Associative Memory Net

Powered by PhDFocusTM