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

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 d...

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Bibliographic Details
Main Authors: Fuentes, M., Garcia-Salicetti, S., Dorizzi, B.
Format: Conference Proceeding
Language:English
Subjects:
Automatic testing
Biometrics
Forgery
Handwriting recognition
Hidden Markov models
Neural networks
Performance evaluation
Support vector machines
System testing
Online Access:Request full text
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Summary: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.
DOI:10.1109/IWFHR.2002.1030918