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%.
This research has been supported by the Fonds Québécois de la Recherche sur la Nature et les Technologies (FQRNT).
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Batista, L., Granger, E., Sabourin, R. (2010). A Multi-Classifier System for Off-Line Signature Verification Based on Dissimilarity Representation. In: El Gayar, N., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2010. Lecture Notes in Computer Science, vol 5997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12127-2_27
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DOI: https://doi.org/10.1007/978-3-642-12127-2_27
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