<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Many handwritten signature verification algorithms have been developed in order to distinguish between genuine signatures and forgeries. An important group of these methods is based on dynamic time warping (DTW). Traditional use of DTW for signature verification consists in forming a misalignment score between the verified signature and a set of template signatures. The right selection of template signatures has a big impact on that verification. In this article, we describe our proposition for replacing the template signatures with the hidden signature-an artificial signature which is created by minimizing the mean misalignment between itself and the signatures from the enrollment set. We present a few hidden signature estimation methods together with their comprehensive comparison. The hidden signature opens a number of new possibilities for signature analysis. We apply statistical properties of the hidden signature to normalize the error signal of the verified signature and to use the misalignment on the normalized errors as a verification basis. A result, we achieve satisfying error rates that allow creating an on-line system, ready for operating in a real-world environment</p></abstract>

Many handwritten signature verification algorithms have been developed in order to distinguish between genuine signatures and forgeries. An important group of these methods is based on dynamic time warping (DTW). Traditional use of DTW for signature verification consists in forming a misalignment score between the verified signature and a set of template signatures. The right selection of template signatures has a big impact on that verification. In this article, we describe our proposition for replacing the template signatures with the hidden signature-an artificial signature which is created by minimizing the mean misalignment between itself and the signatures from the enrollment set. We present a few hidden signature estimation methods together with their comprehensive comparison. The hidden signature opens a number of new possibilities for signature analysis. We apply statistical properties of the hidden signature to normalize the error signal of the verified signature and to use the misalignment on the normalized errors as a verification basis. A result, we achieve satisfying error rates that allow creating an on-line system, ready for operating in a real-world environment

Signature verification: A comprehensive study of the hidden signature method

Faculty of Electronics and Information Technology Warsaw University of Technology, ul. Nowowiejska 15/19, 00-663 Warsaw, Poland

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Signature verification: A comprehensive study of the hidden signature method"}

Many handwritten signature verification algorithms have been developed in order to distinguish between genuine signatures and forgeries. An important group of...

Signature verification: A comprehensive study of the hidden signature method

Published Online: Sep 30, 2015

Page range: 659 - 674

Received: Mar 11, 2014

DOI: https://doi.org/10.1515/amcs-2015-0048

© by Joanna Putz-Leszczyńska

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.