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Autoassociative Signature Authentication Based on Recurrent Neural Network

  • Jun RokuiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10841)

Abstract

In online handwriting authentication, it is difficult to forge handwriting because stroke characteristics cannot be reproduced using only a handwriting trajectory. However, it is difficult to completely reproduce registered stroke characteristics, even when signers attempt to reproduce their own signatures. For this reason, the principal criteria for authentication must be lowered. In this study, we use a recurrent neural network to model the behavior of the musculoskeletal function for handwriting. The proposed model can represent the handwriting stroke process for a character visualized by the authenticator. This research is an anti-counterfeit effort to reduce the error between autoassociative stroke information and handwriting stroke information.

Keywords

Online handwriting authentication Recurrent neural network BPTT 

Notes

Acknowledgments

In this research, I would like to thank S. Sugawara for receiving great advice on data collection and RNN construction.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mathematics and Computer Science Interdisciplinary, Faculty of Science and EngineeringShimane UniversityMatsueJapan

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