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AERFAI Summer School on Deep Learning


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  1. Rico-Juan, J. R.; Iñesta, J. M.
    "Confidence voting method ensemble applied to off-line signature verification"
    Pattern Analysis and Applications, vol. 15, pp. 113--120 (2012)
    : bibtex : pdf

    In this paper, a new approximation to off-line signature verification is proposed based on two-class classifiers using an expert decisions ensemble. Different methods to extract sets of local and a global features from the target sample are detailed. Also a normalisation by confidence voting method is used in order to decrease the final equal error rate (EER). Each set of features is processed by a single expert, and on the other approach proposed, the decisions of the individual classifiers are combined using weighted votes. Experimental results are given using a subcorpus of the large MCYT signature database for random and skilled forgeries. The results show that the weighted combination outperforms the individual classifiers significantly. The best EER obtained were 6.3% in the case of skilled forgeries and 2.3% in the case of random forgeries.

@article {
 author = "Rico-Juan, J. R.; Iñesta, J. M.",
 title  = "Confidence voting method ensemble applied to off-line signature verification",
 issn = "1433-7541",
 journal = "Pattern Analysis and Applications",
 month = "apr",
 number = "2",
 pages = "113--120",
 volume = "15",
 year = "2012"
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