System Maintenance:
There may be intermittent impact on performance while updates are in progress. We apologize for the inconvenience.
By Topic

A New Framework for Adaptive Multimodal Biometrics Management

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)
Kumar, A. ; Dept. Comput., Hong Kong Polytech. Univ., Hung Horn, China ; Kanhangad, V. ; Zhang, D.

This paper presents a new evolutionary approach for adaptive combination of multiple biometrics to ensure the optimal performance for the desired level of security. The adaptive combination of multiple biometrics is employed to determine the optimal fusion strategy and the corresponding fusion parameters. The score-level fusion rules are adapted to ensure the desired system performance using a hybrid particle swarm optimization model. The rigorous experimental results presented in this paper illustrate that the proposed score-level approach can achieve significantly better and stable performance over the decision-level approach. There has been very little effort in the literature to investigate the performance of an adaptive multimodal fusion algorithm on real biometric data. This paper also presents the performance of the proposed approach from the real biometric samples which further validate the contributions from this paper.

Published in:

Information Forensics and Security, IEEE Transactions on  (Volume:5 ,  Issue: 1 )
Biometrics Compendium, IEEE