NDLI: A Robust Online Signature Based Cryptosystem

Content Provider	IEEE Xplore Digital Library
Author	Bhateja, A.K. Chaudhury, S. Saxena, P.K.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. Eng., Indian Inst. of Technol., New Delhi, New Delhi, India (Chaudhury, S.) \|\| Defence R & D Organ., Sci. Anal. Group, New Delhi, India (Bhateja, A.K.; Saxena, P.K.)
Abstract	Cryptography is the backbone for the security systems. The main challenge in use of the Cryptosystems is maintaining the confidentiality of the cryptographic key. A Cryptosystem which encrypts the data using biometric features improves the security of the data and overcomes the problems of key management and key confidentiality. Fuzzy Vault Scheme proposed by Juels and Sudan [1] binds the secret key and the biometric template, so that extraction of the secret without the biometric data is infeasible. Physical signature is a biometric that is widely accepted and is used for proving the authenticity of a person in legal documents, bank transactions etc. Electronic devices such as digital tablets capture azimuth, altitude and pressure along with x any y coordinates at fixed time interval. This paper describes a robust online signature based cryptosystem to hide the secret by binding it with invariant online signature templates. The invariant templates of the signature are derived from artificial neural network based classifier. The entire signature is divided into fixed number of time slices. Important features are extracted based on the consistency of the feature in the slices of the genuine signature. Binary back propagation based neural network for each feature, each subset of slices for a user is trained by a weighted back propagation algorithm. The decisions of these networks are combined using AdaBoost algorithm. The proposed scheme is highly robust as it works well for all kinds of signatures and is independent of the number of zero crossing and high curvature points in the signature trajectory.
Starting Page	79
Ending Page	84
File Size	189783
Page Count	6
File Format	PDF
ISBN	9781479943357
ISSN	21676445
e-ISBN	9781479943340
DOI	10.1109/ICFHR.2014.21
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-01
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cryptography Feature extraction Classification algorithms Polynomials Training Handwriting recognition Decoding cryptosystem online signature variance fuzzy vault back propagation AdaBoost algorithm
Content Type	Text
Resource Type	Article

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