Name
Title | ||
|---|---|---|
Efficient off-line verification and identification of signatures by multiclass support vector machines |
Abstract | ||
|---|---|---|
In this paper we present a novel and efficient approach for off-line signature verification and identification using Support Vector Machine. The global, directional and grid features of the signatures were used. In verification, one-against-all strategy is used. The true acceptance rate is 98% and true rejection rate is 81%. As the identification of signatures represent a multi-class problem, Support Vector Machine's one-against-all and one-against-one strategies were applied and their performance were compared. Our experiments indicate that one-against-one with 97% true recognition rate performs better than one-against-all by 3%. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11556121_98 | CAIP |
Keywords | Field | DocType |
efficient approach,off-line signature verification,one-against-one strategy,true recognition rate,efficient off-line verification,support vector machine,grid feature,one-against-all strategy,multiclass support vector machine,multi-class problem,true acceptance rate,true rejection rate | Off line,Pattern recognition,Character recognition,Computer science,Pattern analysis,Support vector machine,Image processing,Acceptance rate,Artificial intelligence,Rejection rate,Grid | Conference |
ISBN | Citations | PageRank |
3-540-28969-0 | 0 | 0.34 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emre Özgündüz | 1 | 0 | 0.34 |
| Tülin Şentürk | 2 | 0 | 0.34 |
| M. Elif Karsligil | 3 | 73 | 13.69 |