Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
411022 | Neurocomputing | 2006 | 5 Pages |
Abstract
In this work, we propose a multi-matcher method for on-line signature verification that combines bi-class classifiers and one-class classifiers. Global information is extracted with a feature-based representation and recognized by using an ensemble of classifiers. Moreover, we show that methods based on tokenised pseudo-random numbers and user specific signature features are highly dependent upon a parameter, the hashing threshold; we demonstrate that using an ensemble of classifiers it is possible to solve this problem leading to a considerable performance improvement.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Loris Nanni,