Anzeige
Erschienen in:
2010 | OriginalPaper | Buchkapitel
A Multi-Classifier System for Off-Line Signature Verification Based on Dissimilarity Representation
verfasst von : Luana Batista, Eric Granger, Robert Sabourin
Erschienen in: Multiple Classifier Systems
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Although widely used to reduce error rates of difficult pattern recognition problems, multiple classifier systems are not in widespread use in off-line signature verification. In this paper, a two-stage off-line signature verification system based on dissimilarity representation is proposed. In the first stage, a set of discrete HMMs trained with different number of states and/or different codebook sizes is used to calculate similarity measures that populate new feature vectors. In the second stage, these vectors are employed to train a SVM (or an ensemble of SVMs) that provides the final classification. Experiments performed by using a real-world signature verification database (with random, simple and skilled forgeries) indicate that the proposed system can significantly reduce the overall error rates, when compared to a traditional feature-based system using HMMs. Moreover, the use of ensemble of SVMs in the second stage can reduce individual error rates in up to 10%.