Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Fusion in Biometrics
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Multimodal Biometric Authentication Using Quality Signals in Mobile Communications
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Fingerprint verification by fusion of optical and capacitive sensors
Pattern Recognition Letters
Over-complete feature generation and feature selection for biometry
Expert Systems with Applications: An International Journal
Selection of Experts for the Design of Multiple Biometric Systems
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Index driven combination of multiple biometric experts for AUC maximisation
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Hi-index | 0.00 |
Fingerprints are one of the most used biometrics for automatic personal authentication. Unfortunately, it is often difficult to design fingerprint matchers exhibiting the performances required in real applications. To meet the application requirements, fusion techniques based on multiple matching algorithms, multiple fingerprints, and multiple impressions of the same fingerprint, have been investigated. However, no previous work has investigated selection strategies for biometrics. In this paper, a score selection strategy for fingerprint multi-modal authentication is proposed. For each authentication task, only one score is dynamically selected so that the genuine and the impostor users' scores distributions are mainly separated. Score selection is performed by first estimating the likelihood that the input pattern is an impostor or a genuine user. Then, the min score is selected in case of an impostor, while the max score is selected in case of a genuine user. Reported results show that the proposed selection strategy can provide better performances than those of commonly used fusion rules.