Reliability-based voting schemes using modality-independent features in multi-classifier biometric authentication

Authors:
Jonas Richiardi;Andrzej Drygajlo
Affiliations:
Laboratory of IDIAP, Signal Processing Institute, Swiss Federal Institute of Technology Lausanne;Laboratory of IDIAP, Signal Processing Institute, Swiss Federal Institute of Technology Lausanne
Venue:
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Year:
2007

Citing 9
Cited 1

Arbitrating among competing classifiers using learned referees

Knowledge and Information Systems
Random Forests

Machine Learning
Induction of Decision Trees

Machine Learning
Combining Pattern Classifiers: Methods and Algorithms

Combining Pattern Classifiers: Methods and Algorithms
Gaussian Mixture Models for on-line signature verification

WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
Rapid and brief communication: Discriminative multimodal biometric authentication based on quality measures

Pattern Recognition
The BANCA database and evaluation protocol

AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Confidence based gating of multiple face authentication experts

SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Using weighted combination-based methods in ensembles with different levels of diversity

ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I

A supervised method to discriminate between impostors and genuine in biometry

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present three new voting schemes for multiclassifier biometric authentication using a reliability model to influence the importance of each base classifier's vote. The reliability model is a meta-classifier computing the probability of a correct decision for the base classifiers. It uses two features which do not depend directly on the underlying physical signal properties, verification score and difference between user-specific and user-independent decision threshold. It is shown on two signature databases and two speaker databases that this reliability classification can systematically reduce the number of errors compared to the base classifier. Fusion experiments on the signature databases show that all three voting methods (rigged majority voting, weighted rigged majority voting, and selective rigged majority voting) perform significantly better than majority voting, and that given sufficient training data, they also perform significantly better than the best classifier in the ensemble.