By Topic

Nonparametric estimators for online signature authentication

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Ihler, A.T. ; Dept. of Electr. Eng. & Comput. Sci., MIT, Cambridge, MA, USA ; Fisher, J.W. ; Willsky, A.S.

We present extensions to our previous work in modelling dynamical processes. The approach uses an information theoretic criterion for searching over subspaces of the past observations, combined with a nonparametric density characterizing its relation to one-step-ahead prediction and uncertainty. We use this methodology to model handwriting stroke data, specifically signatures, as a dynamical system and show that it is possible to learn a model capturing their dynamics for use either in synthesizing realistic signatures and in discriminating between signatures and forgeries even though no forgeries have been used in constructing the model. This novel approach yields promising results even for small training sets

Published in:

Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on  (Volume:6 )

Date of Conference: