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

Improving biometric identification through quality-based face and fingerprint biometric fusion

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)
Yan Tong ; Visualization & Comput. Vision Lab., GE Global Res., Niskayuna, NY, USA ; Wheeler, Frederick W. ; Xiaoming Liu

Multi-modal biometric fusion is more accurate and reliable compared to recognition using a single biometric modality. However, most existing fusion approaches neglect the influence of the qualities of the biometric samples in information fusion. Our goal is to advance the state-of-the-art in biometric fusion technology by providing a more universal and more accurate solution for personal identification and verification with predictive quality metrics. In this work, we developed score-level multi-modal fusion algorithms based on predictive quality metrics and employed them for the task of face and fingerprint biometric fusion. The causal relationships in the context of the fusion scenario are modeled by Bayesian Networks. The recognition/verification decision is then made through probabilistic inference. Our experiments demonstrated that the proposed score-level fusion algorithms significantly improve the verification performance over the methods based on the raw match score of a single modality (face or fingerprint). Furthermore, the fusion framework with both face and fingerprint image qualities achieves the best verification performance and outperforms all other baseline fusion algorithms tested including other straightforward quality-based fusion methods.

Published in:

Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on

Date of Conference:

13-18 June 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.