By Topic

An integration method of multi-modal biometrics using supervised pareto learning self organizing maps

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

2 Author(s)
Dozono, H. ; Fac. of Sci. & Eng., Saga Univ., Saga ; Nakakuni, M.

This paper proposes a method for the integration of multi-modal biometrics. As the conventional authentication method, password system is mostly used. But, password mechanism has many issues. In order to solve the problems, biometric authentication methods are often used. But, the authentication method using biological characteristics, such as fingerprint, also has some problems. In this paper, we propose a authentication method using multi-modal behavior biometrics sampled from keystroke timings and handwritten patterns. And supervised Pareto learning self organizing maps which integrate the multi-modal vectors is proposed. The performance of this method is confirmed by the authentication experiments.

Published in:

Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on

Date of Conference:

1-8 June 2008