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Online Signature Forgery Prevention
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 75 - Number 13 |
| Year of Publication: 2013 |
| Authors: Shalini Bhatia, Pratik Bhatia, Dheeraj Nagpal, Sandhya Nayak |
10.5120/13172-0849
|
Shalini Bhatia, Pratik Bhatia, Dheeraj Nagpal, Sandhya Nayak . Online Signature Forgery Prevention. International Journal of Computer Applications. 75, 13 ( August 2013), 21-29. DOI=10.5120/13172-0849
Abstract
Personal identity verification is of great importance in today's world which is full of frauds and forgeries. A signature being a very good biometric has been since a long time for personal identity verification. Signature verification is the process used to recognize an individual's handwritten signature to prevent fraud. In this project, the dynamic features of the signature are considered so as to prevent forgeries. The pressure at the pen-tip together with the x and y coordinates of the signature are measured and features extracted from these are used to verify the signature. A signature verification system using Error Back Propagation Training Algorithm is designed using Neural Network Toolbox of MATLAB to verify the signatures. The attractive features of this system are its low cost, low intrusion, good performance and use of an acceptable and natural biometric (the signature). A two-step method is proposed, which involves identification of the signature in the first step followed by individual verification. Both the steps are carried out by Neural Networks trained using Error Back-Propagation Training Algorithm.
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- http://www. cse. ust. hk/svc2004/download. html
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