Neural network-based handwritten signature verification
McCabe, Alan, Trevathan, Jarrod, and Read, Wayne (2008) Neural network-based handwritten signature verification. Journal of Computers, 3 (8). pp. 9-22.
![[img]](https://researchonline.jcu.edu.au/style/images/fileicons/application_pdf.png)
|
PDF (Published Version)
- Published Version
Download (529kB) |
Abstract
Handwritten signatures are considered as the most natural method of authenticating a person's identity (compared to other biometric and cryptographic forms of authentication). The learning process inherent in Neural Networks (NN) can be applied to the process of verifying handwritten signatures that are electronically captured via a stylus. This paper presents a method for verifying handwritten signatures by using a NN architecture. Various static (e.g., height, slant, etc.) and dynamic (e.g., velocity, pen tip pressure, etc.) signature features are extracted and used to train the NN. Several Network topologies are tested and their accuracy is compared. The resulting system performs reasonably well with an overall error rate of 3:3% being reported for the best case.
| Item ID: | 6559 |
|---|---|
| Item Type: | Article (Research - C1) |
| ISSN: | 1796-203X |
| Keywords: | biometrics; neural networks; prediction; type 1 and type 2; artificial intelligence; security |
| Additional Information: | Reproduced with permission from Academy Publisher. © Academy Publisher |
| Date Deposited: | 30 Mar 2010 03:42 |
| FoR Codes: | 08 INFORMATION AND COMPUTING SCIENCES > 0804 Data Format > 080499 Data Format not elsewhere classified @ 100% |
| SEO Codes: | 89 INFORMATION AND COMMUNICATION SERVICES > 8999 Other Information and Communication Services > 899999 Information and Communication Services not elsewhere classified @ 100% |
| Downloads: |
Total: 1734 Last 12 Months: 134 |
| More Statistics |
Actions (Repository Staff Only)
 |
Item Control Page |