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2018 | OriginalPaper | Chapter

Autoassociative Signature Authentication Based on Recurrent Neural Network

Author : Jun Rokui

Published in: Artificial Intelligence and Soft Computing

Publisher: Springer International Publishing

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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.

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Footnotes
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Metadata
Title
Autoassociative Signature Authentication Based on Recurrent Neural Network
Author
Jun Rokui
Copyright Year
2018
Book
Artificial Intelligence and Soft Computing

Print ISBN: 978-3-319-91252-3

Electronic ISBN: 978-3-319-91253-0

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-91253-0_9

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