We propose in this work to perform on-line signature verification by the fusion of two complementary verification modules. The first one considers a signature as a sequence of points and models the genuine signatures of a given signer by a Hidden Markov Model (HMM). Forgeries are used to compute a decision threshold. In the second module, global parameters of a signature are the inputs of a two-classes neural network trained for each signer on both the genuine and "other" signatures (genuine signatures of other signers). Fusion of the scores given by these two experts through a Support Vector Machine (SVM), allows improving the results over those of each module, on Philips' Database.


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    Title :

    On line signature verification: Fusion of a Hidden Markov Model and a neural network via a support vector machine


    Contributors:


    Publication date :

    2002-01-01


    Size :

    335508 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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