IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Person Identification Using Multiple Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expert Conciliation for Multi Modal Person Authentication Systems by Bayesian Statistics
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Large-Scale Evaluation of Multimodal Biometric Authentication Using State-of-the-Art Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
RegionBoost learning for 2D+3D based face recognition
Pattern Recognition Letters
Likelihood Ratio-Based Biometric Score Fusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Over-complete feature generation and feature selection for biometry
Expert Systems with Applications: An International Journal
An automated palmprint recognition system
Image and Vision Computing
Score normalization in multimodal biometric systems
Pattern Recognition
Handbook of Multibiometrics
A classification approach to multi-biometric score fusion
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Combining multiple matchers for fingerprint verification: a case study in FVC2004
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Exploiting global and local decisions for multimodal biometrics verification
IEEE Transactions on Signal Processing - Part II
Score level fusion of multimodal biometrics using triangular norms
Pattern Recognition Letters
Hi-index | 12.05 |
In this work, we present a novel trained method for combining biometric matchers at the score level. The new method is based on a combination of machine learning classifiers trained using the match scores from different biometric approaches as features. The parameters of a finite Gaussian mixture model are used for modelling the genuine and impostor score densities during the fusion step. Several tests on different biometric verification systems (related to fingerprints, palms, fingers, hand geometry and faces) show that the new method outperforms other trained and non-trained approaches for combining biometric matchers. We have tested some different classifiers, support vector machines, AdaBoost of neural networks, and their random subspace versions, demonstrating that the choice for the proposed method is the Random Subspace of AdaBoost.