CFP last date
22 April 2024
Reseach Article

Offline Signature Verification using Feature Point Extraction

by S.N. Gunjal, B.J. Dange, A.V. Brahmane
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 141 - Number 14
Year of Publication: 2016
Authors: S.N. Gunjal, B.J. Dange, A.V. Brahmane
10.5120/ijca2016909852

S.N. Gunjal, B.J. Dange, A.V. Brahmane . Offline Signature Verification using Feature Point Extraction. International Journal of Computer Applications. 141, 14 ( May 2016), 6-12. DOI=10.5120/ijca2016909852

@article{ 10.5120/ijca2016909852,
author = { S.N. Gunjal, B.J. Dange, A.V. Brahmane },
title = { Offline Signature Verification using Feature Point Extraction },
journal = { International Journal of Computer Applications },
issue_date = { May 2016 },
volume = { 141 },
number = { 14 },
month = { May },
year = { 2016 },
issn = { 0975-8887 },
pages = { 6-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume141/number14/24850-2016909852/ },
doi = { 10.5120/ijca2016909852 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:43:32.345903+05:30
%A S.N. Gunjal
%A B.J. Dange
%A A.V. Brahmane
%T Offline Signature Verification using Feature Point Extraction
%J International Journal of Computer Applications
%@ 0975-8887
%V 141
%N 14
%P 6-12
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Signature verification is one of the most widely used biometrics for authentication. The objective of the signature verification system is to discriminate between two classes: the original and the forgery, which are related to intrapersonal and interpersonal variability. Firstly, there exists great variation even between two signatures of the same person. They never start from the same position and neither do they terminate at the same position. Also, the angle of inclination of the signatures, the relative spacing between letters of the signatures, height of letters, all vary even for the same person. Hence it becomes a challenging task to compare between two signatures of the same person. The proposed an offline signature verification system to take care of that, which is based on depth for segmentation of signature image into different parts, after that geometric center of the each segment is find out as the feature point of that segment. The number of feature points extracted from signature image is equivalent to the number segment of the signature image that is produce by specifying value of depth. The classification of the feature points utilizes two statistical parameters like mean and variance. Our proposed model has three stages: image pre-processing, feature point’s extraction and classification & verification. The user introduces into the computer through scanned signature images, our technique modifies their quality by image enhancement and noise reduction techniques, to be followed by feature extraction and finally used Euclidean distance model to classification of signature either genuine or forgery. The proposed offline signature verification system used “GPDS360 signature database”.

References
  1. Swati Srivastava and Suneeta Agarwal, “Offline Signature Verification using Grid based Feature Extraction”, International Conference on Computer & Communication Technology (ICCCT)-2011 IEEE.
  2. Suhail M. Odeh and Manal Khalil, “Off-line signature verification and recognition: Neural Network Approach”, 2011 IEEE.
  3. Ashwini Pansare and Shalini Bhatia, “Handwritten Signature Verification using Neural Network” , International Journal of Applied Information Systems (IJAIS).
  4. 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.
  5. 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.
  6. Tai-Ping Zhang, Bin Fang, Bin Xu, Heng-Xin Chen, Miao Chen and Yuan-Yan Tang, “Signature Envelope Curvature Descriptor For Offline Signature Verification”, Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition,-2007 IEEE.
  7. Dakshina Ranjan Kisku, Ajita Rattani, Phalguni Gupta and Jamuna Kanta Sing, “ Offline Signature Verification using Geometric and Orientation Features with Multiple Experts Fusion”, International Conference on Electronic Computer Technology (ICECT)-2011 IEEE.
  8. Alberto Martin and Sabri Tosunoglu, “Image Processing Techniques For Machine Vision”, Conference on Recent Advances in Robotics (ICRAR).
  9. 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
  10. 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.
  11. Dr. S. Adebayo Daramola and Prof. T. Samuel Ibiyemi, “Offline Signature Recognition using Hidden Markov Model (HMM) ”, International Journal of Computer Applications..
  12. Debnath Bhattacharyya, Rahul Ranjan, Farkhod Alisherov A, and Minkyu Choi, “Biometric Authentication: A Review”, International Journal of u- and e- Service, Science and Technology, 2009.
  13. Igor Bohm and Florian Testor, “Biometric Systems”, Department of Telecooperation University of Linz 4040 Linz, Austria,2006
Index Terms

Computer Science
Information Sciences

Keywords

Offline signature verification geometric centre feature point forgeries FAR(False Acceptance Rate) FRR (False Rejection Rate) CCR (Correct Classification Rate) image processing.