Fusion of biometric algorithms in the recognition problem

Authors:
Andrew L. Rukhin;Igor Malioutov
Affiliations:
Statistical Engineering Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA and Department of Mathematics and Statistics, University of Maryland, Baltimore County ...;Statistical Engineering Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
Venue:
Pattern Recognition Letters
Year:
2005

Citing 7
Cited 3

Linear programming and network flows (2nd ed.)

Linear programming and network flows (2nd ed.)
Decision Combination in Multiple Classifier Systems

IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal combinations of pattern classifiers

Pattern Recognition Letters
On Combining Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review

IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multimodal biometrics: issues in design and testing

Proceedings of the 5th international conference on Multimodal interfaces

Fusing multimodal biometrics with quality estimates via a Bayesian belief network

Pattern Recognition
Fusion of face and iris features for multimodal biometrics

ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Biometrics Technology and the New Economy: A Review of the Field and the Case of the United Arab Emirates

International Journal of Innovation in the Digital Economy

Quantified Score

Hi-index	0.10

Visualization

Abstract

This note concerns the mathematical aspects of fusion for several biometric algorithms in the recognition or identification problem. It is assumed that a biometric signature is presented to a system which compares it with a database of signatures of known individuals (gallery). On the basis of this comparison, an algorithm produces the similarity scores of this probe to the signatures in the gallery, which are then ranked according to their similarity scores of the probe. The suggested procedures define several versions of aggregated rankings. An example from the Face Recognition Technology (FERET) program with four recognition algorithms is considered.