Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information fusion in biometrics
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Communications of the ACM - Multimodal interfaces that flex, adapt, and persist
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Quality-based Score Level Fusion in Multibiometric Systems
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Likelihood Ratio-Based Biometric Score Fusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combination of Experts by Classifiers in Similarity Score Spaces
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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
A principled approach to score level fusion in multimodal biometric systems
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
| Hi-index | 0.00 |
Multimodal biometric systems integrate information from multiple sources to improve the performance of a typical unimodal biometric system. Among the possible information fusion approaches, those based on fusion of match scores are the most commonly used. Recently, a framework for the optimal combination of match scores that is based on the likelihood ratio (LR) test has been presented. It is based on the modeling of the distributions of genuine and impostor match scores as a finite Gaussian mixture models. In this paper, we propose two strategies for improving the performance of the LR test. The first one employs a voting strategy to circumvent the need of huge datasets for training, while the second one uses a sequential test to improve the classification accuracy on genuine users. Experiments on the NIST multimodal database confirmed that the proposed strategies can outperform the standard LR test, especially when there is the need of realizing a multibiometric system that must accept no impostors.