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Combining Snakes and Neural Networks for Off-Line Signature Verification

Abstract : This paper introduces an improved snake algorithm based on the work by Kass et al. Our approach is applied to the off-line signature verification problem where signatures are scanned and then converted into binary images. This way no dynamic information of the signers is available. We also have considered some real conditions for the verification problem when applied to bank check. For example our system uses only one training signature per subject. Involved system parameters are tuned to solve the task in an effective and efficient manner. A two-layer perceptron is build for signature classification and it uses only two signature features (distance and matching factor) provided by the adjusted snake. Finally, a study of the system for a signature database is provided.
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https://hal.inria.fr/inria-00103939
Contributor : Anne Jaigu <>
Submitted on : Thursday, October 5, 2006 - 3:16:55 PM
Last modification on : Thursday, October 5, 2006 - 3:47:48 PM
Long-term archiving on: : Tuesday, April 6, 2010 - 6:31:37 PM

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  • HAL Id : inria-00103939, version 1

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José F. Vélez, Ángel Sánchez, Ana B. Moreno, José L. Esteban. Combining Snakes and Neural Networks for Off-Line Signature Verification. Tenth International Workshop on Frontiers in Handwriting Recognition, Université de Rennes 1, Oct 2006, La Baule (France). ⟨inria-00103939⟩

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