Name
Abstract | ||
|---|---|---|
In this paper we describe a new approach to dynamic signature verification using the discriminative training framework. The authentic and forgery samples are represented by two separate Gaussian Mixture models and discriminative training is used to achieve optimal separation between the two models. An enrollment sample clustering and screening procedure is described which improves the robustness of the system. We also introduce a method to estimate and apply subject norms representing the "typical": variation of the subject's signatures. The subject norm functions are parameterized, and the parameters are trained as an integral part of the discriminative training. The system was evaluated using 480 authentic signature samples and 260 skilled forgery samples from 44 accounts and achieved an equal error rate of 2.25%. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICDAR.2005.95 | ICDAR-1 |
Keywords | Field | DocType |
equal error rate,subject norm function,skilled forgery sample,forgery sample,subject norm,dynamic signature verification,discriminative training,authentic signature sample,discriminative training framework,enrollment sample clustering,gaussian mixture model,gaussian processes,handwriting recognition | Parameterized complexity,Pattern recognition,Computer science,Word error rate,Handwriting recognition,Speech recognition,Robustness (computer science),Gaussian process,Artificial intelligence,Cluster analysis,Discriminative model,Mixture model | Conference |
ISSN | ISBN | Citations |
1520-5363 | 0-7695-2420-6 | 3 |
PageRank | References | Authors |
0.43 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gregory F. Russell | 1 | 20 | 1.46 |
| Jianying Hu | 2 | 478 | 35.52 |
| Alain Biem | 3 | 288 | 18.64 |
| Andre Heilper | 4 | 14 | 1.46 |
| Dmitry Markman | 5 | 3 | 0.43 |