Name
Abstract | ||
|---|---|---|
In this paper a novel offline signature verification scheme has been proposed. The scheme is based on selecting 60 feature points from the geometric centre of the signature and compares them with the already trained feature points. The classification of the feature points utilizes statistical parameters like mean and variance. The suggested scheme discriminates between two types of originals and forged signatures. The method takes care of skill, simple and random forgeries. The objective of the work is to reduce the two vital parameters False Acceptance Rate (FAR) and False Rejection Rate (FRR) normally used in any signature verification scheme. In the end comparative analysis has been made with standard existing schemes. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/COGINF.2008.4639204 | Journal of Computer Science |
Keywords | Field | DocType |
computational geometry,feature extraction,fraud,handwriting recognition,pattern classification,statistical analysis,FAR,FRR,false acceptance rate,false rejection rate,feature point extraction method,geometric centre,improved offline signature verification scheme,random forgeries,statistical parameters,Euclidean Distance Model,FAR (False Acceptance Rate),FRR (False Rejection Rate),Feature point,Forgeries,Geometric centre,Offline signature | Statistical parameter,Data mining,False rejection rate,Pattern recognition,Computer science,Acceptance rate,Artificial intelligence,Machine learning | Conference |
Volume | Issue | ISBN |
4 | 2 | 978-1-4244-2538-9 |
Citations | PageRank | References |
1 | 0.38 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Debasish Jena | 1 | 44 | 8.15 |
| Banshidhar Majhi | 2 | 356 | 49.76 |
| Saroj Kumar Panigrahy | 3 | 4 | 1.47 |
| Sanjay Kumar Jena | 4 | 101 | 14.37 |