CFP last date
20 May 2024
Reseach Article

Offline Signature Verification in Punjabi based on SURF Features and Critical Point Matching using HMM

by Rajpal Kaur, Pooja Choudhary
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 111 - Number 16
Year of Publication: 2015
Authors: Rajpal Kaur, Pooja Choudhary
10.5120/19620-1288

Rajpal Kaur, Pooja Choudhary . Offline Signature Verification in Punjabi based on SURF Features and Critical Point Matching using HMM. International Journal of Computer Applications. 111, 16 ( February 2015), 4-11. DOI=10.5120/19620-1288

@article{ 10.5120/19620-1288,
author = { Rajpal Kaur, Pooja Choudhary },
title = { Offline Signature Verification in Punjabi based on SURF Features and Critical Point Matching using HMM },
journal = { International Journal of Computer Applications },
issue_date = { February 2015 },
volume = { 111 },
number = { 16 },
month = { February },
year = { 2015 },
issn = { 0975-8887 },
pages = { 4-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume111/number16/19620-1288/ },
doi = { 10.5120/19620-1288 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:48:02.776623+05:30
%A Rajpal Kaur
%A Pooja Choudhary
%T Offline Signature Verification in Punjabi based on SURF Features and Critical Point Matching using HMM
%J International Journal of Computer Applications
%@ 0975-8887
%V 111
%N 16
%P 4-11
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Biometrics, which refers to identifying an individual based on his or her physiological or behavioral characteristics, has the capabilities to the reliably distinguish between an authorized person and an imposter. The Signature recognition systems can categorized as offline (static) and online (dynamic). This paper presents Surf Feature based recognition of offline signatures system that is trained with low-resolution scanned signature images. The signature of a person is an important biometric attribute of a human being which can be used to authenticate human identity. However the signatures of human can be handled as an image and recognized using computer vision and HMM techniques. With modern computers, there is need to develop fast algorithms for signature recognition. There are multiple techniques are defined to signature recognition with a lot of scope of research. In this paper, (static signature) off-line signature recognition & verification using surf feature with HMM is proposed, where the signature is captured and presented to the user in an image format. Signatures are verified depended on parameters extracted from the signature using various image processing techniques. The Off-line Signature Verification and Recognition is implemented using Mat lab platform. This work has been analyzed or tested and found suitable for its purpose or result. The proposed method performs better than the other recently proposed methods.

References
  1. Anil K Jain, Arun Ross and Salil Prabhakar, "An Introduction to Biometric Recognition,"IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 1-29, 2004.
  2. K. R. Radhika, M. K. Venkatesha and G. N. Sekhar, "Off-Line Signature Authentication Based on Moment Invariants Using Support Vector Machine", Journal of Computer Science 6 (3): 305-311, 2010.
  3. Reza Ebrahimpour, Ali Amiri, Masoom Nazari and Alireza Hajiany, "Robust Modelfor Signature Recognition Based on Biological Inspired Features", International Journal of Computer and Electrical Engineering, Vol. 2, No. 4, August, 2010.
  4. A. Piyush Shanker, A. N. Rajagopalan, "Off-line signature verification using DTW", Pattern Recognition Letters 28 (2007) 1407–1414.
  5. Mohammed A. Abdala & Noor Ayad Yousif, Offline Signature Recognition and Verification Based on Artificial Neural Network, Engineering & Technology Journal, Vol. 27, No. 7, 2009.
  6. L. Basavaraj and R. D Sudhakar Samuel, Offline line signature Verification and Recognition: An Approach Based on Four Speeds Stroke Angle, International Journal of Recent Trends in Engineering, Vol 2, No. 3, November 2009.
  7. H. Baltzakis, N. Papamarkos, A new signature verification technique based on a two stage neural network classifier, Engineering Applications of Artificial Intelligence 14 (2001) 95-103.
  8. Jesus F. Vargas, Miguel A. Ferrer, Carlos M. Travieso, Jesus B. Alonso, Offline Signature Verification Based on Pseudo Cepstral Coefficients, 10th International Conference on Document Analysis And Recognition 2009.
  9. Ashwini Pansare, Shalini Bhatia, Handwritten Signature Verification Using Neural Network, International Journal of Applied Information Systems (IJAIS) ISSN: 2249 0868 Foundation of Computer Science FCS, New York, USA Volume 1 No. 2, January 2012.
  10. Jean Baptiste Fasquel and Michel Bruynooghe, A hybrid opto- Electronic method for Real time automatic verification of handwritten Signatures, Digital Image Computing Techniques and Applications,21, 22 January 2002, Melbourne, Australia.
  11. Julio Martínez R. , Rogelio Alcántara S. , Online signature Verification Based on Optimal feature representation and neural Network driven Fuzzy reasoning.
  12. Prashanth C. R. and K. B. Raja, Offline Signature Verification Based On Angular Features, International Journal of Modeling and Optimization, Vol. 2, No. 4, August 2012.
  13. Shashi Kumar D R, K B Raja, R. K Chattaroy, Sabyasachi Pattanaik, Offline Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks, International Journal of Engineering Science and Technology Vol. 2(12), 2010, 7035-7044.
  14. J. F. Vargas, M. A. Ferrer, C. M. Travieso, and J. B. Alonso. Offline Signature Verification based on pseudo-cepstral coefficients. 10th IEEE Int Conf on Document Anal. & Recognition, 2009.
  15. A. I. Al-Shoshan. Handwritten signature verification using image Invariants and Dynamic features. Proc of the IEEE Int Conf on Computer Graphics, Imaging and Visualization (CGIV'06), 2006.
  16. M. Piekarczyk. Hierarchical random graph model for off-line handwritten signatures Recognition. IEEE Int Conf on Complex, Intelligent, Software Intensive Systems, 2010.
  17. S. M. S. Ahmad, A. Shakil, M. A. Faudzi, R. M. Anwar. Analysis of 'Goat' within user population of an offline signature biometrics. 10th IEEE Int Conf on Information Science, Signal Processing and their Applications (ISSPA 2010).
  18. J. P. Drouhard, R. Sabourin, and M. Godbout. A neural network Approach to off-line Signature verification using directional PDF. Pattern Recognition, 29(3), (1996), 415--424.
  19. Anil K Jain, Arun Ross and Salil Prabhakar, "An Introduction to Biometric Recognition,"IEEE Transactions on Circuits and Systems For Video Technology, vol. 14, no. 1, pp. 1-29, 2004.
  20. Vahid Malekian, Alireza Aghaei, Mahdie Rezaeian and Mahmood Alian, "rapid Offline signature verification base on signature envelope And density partioning" IEEE, 2013.
  21. Ali Karouni, Bassam Daya, samia Bahlak, "offline signature recognition Using neural Network approach" A. Karouni et al. /Procedia Computer Science 3 (2011) 155–161.
  22. Vn Nguyen, Michael Blumenstein Graham Leedham, "global feature For offline Signature verification problems" 10th international Conference on document Analysis and recognition, 2009
Index Terms

Computer Science
Information Sciences

Keywords

Offline Signature verification offline signature recognition signatures SURF features and HMM.