Cart (Loading....) | Create Account
Close category search window

A Postural Information-Based Biometric Authentication System Employing S-Transform, Radial Basis Function Network, and Extended Kalman Filtering

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

4 Author(s)
Chatterjee, A. ; Lab. Images, Signaux et Syst. Intelligents (LiSSi, EA 3956), Univ. Paris XII - Val de Marne, Créteil, France ; Fournier, R. ; Nait-Ali, A. ; Siarry, P.

This paper proposes a new system for biometry-based human authentication, where postural signal information is utilized to identify a person. The system employs a novel approach where four types of temporal postural signals are acquired for each person to develop an authentication database, and for each posture, both signals in the - and -directions are utilized for the purpose of authentication. The proposed system utilizes S-transform, which is a joint time-frequency representation tool, to determine the characteristic features for each human posture. Based on these characteristic features, a radial basis function network (RBFN) system is developed for the purpose of specific authentication. The RBFN authentication system is developed by training it to employ extended Kalman filtering (EKF). The EKF-trained RBFN authentication system could produce overall authentication accuracy on the order of 94%-95% and could outperform similar authentication systems developed, which employ two very popular variants of backpropagation neural networks (BPNNs) and a variant of radial basis neural network (RBNN).

Published in:

Instrumentation and Measurement, IEEE Transactions on  (Volume:59 ,  Issue: 12 )

Date of Publication:

Dec. 2010

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.