Sciweavers

Share
MCS
2010
Springer

A Multi-Classifier System for Off-Line Signature Verification Based on Dissimilarity Representation

10 years 6 months ago
A Multi-Classifier System for Off-Line Signature Verification Based on Dissimilarity Representation
Abstract. 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 featurebased system using HMMs. Moreover, the use of ensemble of SVMs in the second stage can reduce individual error rates in up to 10%.
Luana Batista, Eric Granger, Robert Sabourin
Added 14 Oct 2010
Updated 14 Oct 2010
Type Conference
Year 2010
Where MCS
Authors Luana Batista, Eric Granger, Robert Sabourin
Comments (0)
books