Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Quality-based Score Level Fusion in Multibiometric Systems
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Performance of Biometric Quality Measures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Biometrics
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Score normalization in multimodal biometric systems
Pattern Recognition
An experimental comparison of different methods for combining biometric identification systems
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Fusion of finger types for fingerprint indexing using minutiae quadruplets
Pattern Recognition Letters
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Multibiometric systems fuse evidences from multiple biometric sources typically resulting in better recognition accuracy. These systems can consolidate information at various levels. For systems operating in the identification mode, rank level fusion presents a viable option. In this paper, several simple but powerful modifications are suggested to enhance the performance of rank-level fusion schemes in the presence of weak classifiers or low quality input images. These modifications do not require a training phase, therefore making them suitable in a wide range of applications. Experiments conducted on a multimodal database consisting of a few hundred users indicate that the suggested modifications to the highest rank and Borda count methods significantly enhance the rank-1 accuracy. Experiments also reveal that including image quality in the fusion scheme enhances the Borda count rank-1 accuracy by ∼ 40%.