A multi-classifier system for off-line signature verification based on dissimilarity representation

Authors:
Luana Batista;Eric Granger;Robert Sabourin
Affiliations:
Laboratoire d'imagerie, de vision et d'intelligence artificielle, École de technologie supérieure, Montreál, QC, Canada;Laboratoire d'imagerie, de vision et d'intelligence artificielle, École de technologie supérieure, Montreál, QC, Canada;Laboratoire d'imagerie, de vision et d'intelligence artificielle, École de technologie supérieure, Montreál, QC, Canada
Venue:
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Year:
2010

Citing 9
Cited 3

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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 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%.