CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Article:Offline Signature Recognition using Hidden Markov Model (HMM)

by Dr. S. Adebayo Daramola, Prof. T. Samuel Ibiyemi
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 10 - Number 2
Year of Publication: 2010
Authors: Dr. S. Adebayo Daramola, Prof. T. Samuel Ibiyemi
10.5120/1454-1967

Dr. S. Adebayo Daramola, Prof. T. Samuel Ibiyemi . Article:Offline Signature Recognition using Hidden Markov Model (HMM). International Journal of Computer Applications. 10, 2 ( November 2010), 17-22. DOI=10.5120/1454-1967

@article{ 10.5120/1454-1967,
author = { Dr. S. Adebayo Daramola, Prof. T. Samuel Ibiyemi },
title = { Article:Offline Signature Recognition using Hidden Markov Model (HMM) },
journal = { International Journal of Computer Applications },
issue_date = { November 2010 },
volume = { 10 },
number = { 2 },
month = { November },
year = { 2010 },
issn = { 0975-8887 },
pages = { 17-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume10/number2/1454-1967/ },
doi = { 10.5120/1454-1967 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:58:44.427690+05:30
%A Dr. S. Adebayo Daramola
%A Prof. T. Samuel Ibiyemi
%T Article:Offline Signature Recognition using Hidden Markov Model (HMM)
%J International Journal of Computer Applications
%@ 0975-8887
%V 10
%N 2
%P 17-22
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

HMM has been used successfully to model speech and online signature in the past two decades. The success has been attributed to the fact that these biometric traits have time reference. Only few HMM based offline signature recognition systems have be developed because offline signature lack time reference. This paper presents a recognition system for offline signatures using Discrete Cosine Transform (DCT) and Hidden Markov Model (HMM). The signature to be trained or recognized is vertically divided into segments at the centre of gravity using the space reference positions of the pixels. The number of segmented signature blocks is equal to the number of states in the HMM for each user notwithstanding the length of the signatures. Experimental result shows that successful signatures recognition rates of 99.2% is possible. The result is better in comparison with previous related systems based on HMM and statistical classifiers.

References
  1. R. Plamondon and S.N. Srihari. 2000. “On-line and off-line Handwriting Recognition: A comprehensive Survey”, IEE tran. on Pattern Analysis and Machine Intelligence, Vol. 22, no.1, pp. 63-84.
  2. F. Leclerc and R. Plamondon. 1994. “Automatic Verification and Writer Identification: The State of the Art 1989-1993”, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 8, pp. 643 – 660.
  3. R. Sabourin. 1997. “Off-line signature verification: Recent advances and perspectives”, BSDIA’97, pp.84-98.
  4. J. P. Edward. 2002. “Customer Authentication-The Evolution of Signature Verification in Financial Institutions”, Journal of Economic Crime Management, Volume 1, Issue 1.
  5. J. Coetzer, B.M. Herbst and J.A. Du Preez. 2004. “Off-line Signature Verification Using the Discrete Radon Transform and a Hidden Markov Model”, Eurasip Journal on Applied Signal Processing - Special Issue on Biometric Signal Processing, Vol. 100, No. 4, pp. 559-571.
  6. V. Kiani, R. Pourreza and H. R Pourreza. 2010. “Offline Signature Verification Using Local Radon Transform and Support Vector Machines”, International journal of Image Processing (IJIP) Vol.(3), Issue(5).
  7. V.V Kohir and U.B. Desai. 1998. “ Face Recognition Using A DCT-HMM Approach”, Fourth IEEE workshop on Applications of Computer vision (WACV’98).
  8. E. Yacoubi, E.J.R. Justino, R. Sabourin and F. Bortolozzi. 2000. “Off-line signature verification using HMMs and cross-validation”, IEEE International Workshop in Neural Networks for Signal Processing, pp. 859-868.
  9. E. Justino, F. Bortolozzi and R. Sabourin. 2001. “Off-line signature verification using HMM for random, simple and skilled forgeries”, Proceedings of Sixth International Conference on Document Analysis and Recognition, Vol. 1, pp. 1031-1034.
  10. E. Justino, F. Bortolozzi and R. Sabourin. 2005. “Comparison of SVM and HMM classifiers in the off-line signature verification”, Pattern Recognition Letters, pp. 1377-1385.
  11. E. Justino, A. Yacoubi, R. Sabourin and F. Bortolozzi. 2000. “An off-line signature verification system using HMM and graphometric features”, Proc. of the 4th International Workshop on Document Analysis Systems, pp. 211-222.
  12. M. Banshider, R .Y Santhosh and B .D Prasanna. 2006. “Novel features for off-line signature verification” International Journal of Computers, Communications & Control ,Vol. 1 , No. 1, pp. 17-24.
  13. S. Daramola and S. Ibiyemi. 2010. “Novel Feature Extraction Technique for Offline Signature verification”, International Journal of Engineering Science and Technology, Vol (2)7, pp 3137-3143.
  14. S. Daramola and S. Ibiyemi. 2010. “Person Identification System using Static and dynamic Signature Fusion”, International Journal of Computer Science and Information Security, Vol (6)7, pp88-92.
  15. L. R. Rabiner. 1989. “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” Proceeding of the IEEE, Vol 77, pp. 257-286.
  16. L.R. Rabiner and B.H. Juang. 1993. “Fundamentals of Speech Recognition”, Prentice Hall,
Index Terms

Computer Science
Information Sciences

Keywords

Offline Signature DCT Features Hidden Markov Model

Powered by PhDFocusTM