Offline Signature Verification Using Various Methods: A Review
Keywords:
DCT, FAR, FRR, signature verification, features, PMTAbstract
Offline signature verification was the first approach to be applied for solving the signature verification problem. It involves the discrimination of genuine and forged signatures on static images. Unlike online systems, offline systems have only the static image containing the signature as an input, without having any knowledge on the signing process. Some difficulties that may arise in offline systems are related to the scanning process (noise on the image) and to the signature acquisition process where different pen tips and widths can produce different shapes. This paper presents a brief study on offline signature verification and recognition using various methods, FAR and FRR is calculated for each method and results are compared.
References
Ali Karouni,Bassam Daya,Samia Bahlak, “Offline signature recognition using neural networks approach” Procedia Computer Science Vol 3, pp 155–161,2011
H. Baltzakis, N. Papamarkos, “A new signature verification technique based on a two-stage neural network classifier”, Engineering Applications of Artificial Intelligence ,2001.
Odeh, S.M and Khalil,M., " Apply Multi-Layer Perceptrons Neural Network for Off-line signature verification and recognition ", IEEE transactions on pattern analysis and machine intelligence, Vol 27, pp 235- 247, June 2011.
P. Deng, H. Yuan Mark Liao & H. Tyan, “Wavelet Based Off-line Signature Recognition System”, Proceedings 5th Conference on Optical Character Recognition and Document Analysis, Beijing,China,1996
A. Piyush Shanker and A. N. Rajagopalan, “Off-line signature verification using DTW, Pattern Recognition Letters”, Vol.28 n.12, pp 1407-1414,2007
Indrajit Bhattacharya bir GhoshSwarup Biswas,“Offline Signature
,
Verification Using Pixel Matching Technique” Procedia Computer Science Vol 2,pp 134-142 ,2013.
Prashanth C. R. and K. B. Raja,” Off-line Signature Verification Based on Angular Features”, International Journal of Modeling and Optimization, Vol. 2, No. 4, August 2012. S. Rashidi
A. Fallah, F. Towhidkhah,”Feature extraction based DCTon dynamic signature verification” Procedia Computer Science Vol 3, pp 1810–1819 December 2012.
S. Audet, P. Bansal, and S. Baskaran ,“Off-line signature verification using virtual support vector machines”, ECSE 526 – Artificial Intelligence, April 7, 2006.
Ibrahim S. I. Abuhaiba ,”Offline 10.Signature Verification Using Graph Matching” Turk J Elec Engin, VOL.15, NO.1 2007.
H. Lv, W. Wang, C. Wang, and Q. Zhou,”Off-line Chinese signature verification based on support vector machines”, PRL , vol. 26, no. 15, pp. 2390–2399 ,2005
J.Edson, R.Justino, F.Bortolozzi and R. Sabourin, “Off-line signature verification using HMM for Random, Simple and Skilled Forgeries”, 2001 13. B. Majhi, Y. Reddy, D. Babu, “Novel Features for Off-line Signature Verification”, International Journal of Computers,.Communications & Control Vol.I (2006), No. 1, pp. 17-24,2012
L. Basavaraj and R. D Sudhaker Samuel, “Offline-line Signature Verification and Recognition: An Approach Based on Four Speed Stroke Angle”, International Journal of Recent Trends in Engineering, Vol 2, No. 3, November 2009
V.E. Ramesh, M. Narasimha Murty ,” Off-line signature verification using genetically optimized weighted features”, Pattern RecognitionVol.32, Issue 2,, pp.217–233,February 1999
Tulsi Gupta ,“Off-line Signature Verification”, INTERNATIONAL JOURNAL OF COMPUTER APPLICATION Vol. 3 ISSUE 2, JUNE 2012.
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Trinity Journal of Management, IT & Media (TJMITM)
This work is licensed under a Creative Commons Attribution 4.0 International License.
