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

Marc Fuentes; Sonia Garcia-Salicetti; Bernadette Dorizzi

doi:10.1109/IWFHR.2002.1030918

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

Marc Fuentes, Sonia Garcia-Salicetti, Bernadette Dorizzi

CNRS SAMOVAR UMR 5157

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	Proceedings - 8th International Workshop on Frontiers in Handwriting Recognition, IWFHR 2002
Publisher	IEEE Computer Society
Pages	253-258
Number of pages	6
ISBN (Print)	0769516920, 9780769516929
DOIs	https://doi.org/10.1109/IWFHR.2002.1030918
Publication status	Published - 1 Jan 2002
Externally published	Yes
Event	8th International Workshop on Frontiers in Handwriting Recognition, IWFHR 2002 - Ontario, ON, Canada Duration: 6 Aug 2002 → 8 Aug 2002

Publication series

Name	Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR
ISSN (Print)	1550-5235

Conference

Conference	8th International Workshop on Frontiers in Handwriting Recognition, IWFHR 2002
Country/Territory	Canada
City	Ontario, ON
Period	6/08/02 → 8/08/02

Access to Document

10.1109/IWFHR.2002.1030918

Cite this

Fuentes, M., Garcia-Salicetti, S., & Dorizzi, B. (2002). On line signature verification: Fusion of a Hidden Markov Model and a neural network via a support vector machine. In Proceedings - 8th International Workshop on Frontiers in Handwriting Recognition, IWFHR 2002 (pp. 253-258). (Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR). IEEE Computer Society. https://doi.org/10.1109/IWFHR.2002.1030918

Fuentes, Marc ; Garcia-Salicetti, Sonia ; Dorizzi, Bernadette. / On line signature verification : Fusion of a Hidden Markov Model and a neural network via a support vector machine. Proceedings - 8th International Workshop on Frontiers in Handwriting Recognition, IWFHR 2002. IEEE Computer Society, 2002. pp. 253-258 (Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR).

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

abstract = "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.",

author = "Marc Fuentes and Sonia Garcia-Salicetti and Bernadette Dorizzi",

year = "2002",

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doi = "10.1109/IWFHR.2002.1030918",

language = "English",

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series = "Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR",

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pages = "253--258",

booktitle = "Proceedings - 8th International Workshop on Frontiers in Handwriting Recognition, IWFHR 2002",

note = "8th International Workshop on Frontiers in Handwriting Recognition, IWFHR 2002 ; Conference date: 06-08-2002 Through 08-08-2002",

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Fuentes, M, Garcia-Salicetti, S & Dorizzi, B 2002, On line signature verification: Fusion of a Hidden Markov Model and a neural network via a support vector machine. in Proceedings - 8th International Workshop on Frontiers in Handwriting Recognition, IWFHR 2002. Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR, IEEE Computer Society, pp. 253-258, 8th International Workshop on Frontiers in Handwriting Recognition, IWFHR 2002, Ontario, ON, Canada, 6/08/02. https://doi.org/10.1109/IWFHR.2002.1030918

On line signature verification: Fusion of a Hidden Markov Model and a neural network via a support vector machine. / Fuentes, Marc; Garcia-Salicetti, Sonia; Dorizzi, Bernadette.
Proceedings - 8th International Workshop on Frontiers in Handwriting Recognition, IWFHR 2002. IEEE Computer Society, 2002. p. 253-258 (Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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.

AB - 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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Fuentes M, Garcia-Salicetti S, Dorizzi B. On line signature verification: Fusion of a Hidden Markov Model and a neural network via a support vector machine. In Proceedings - 8th International Workshop on Frontiers in Handwriting Recognition, IWFHR 2002. IEEE Computer Society. 2002. p. 253-258. (Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR). doi: 10.1109/IWFHR.2002.1030918

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

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