Fingerprint verification by fusion of optical and capacitive sensors
Pattern Recognition Letters
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
ICCSA '10 Proceedings of the 2010 International Conference on Computational Science and Its Applications
GUC100 multisensor fingerprint database for in-house (semipublic) performance test
EURASIP Journal on Information Security
Combining multiple matchers for fingerprint verification: a case study in FVC2004
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
How do correlation and variance of base-experts affect fusion in biometric authentication tasks?
IEEE Transactions on Signal Processing
Hi-index | 0.00 |
This paper presents our study on fingerprint fusion in particular in three scenario sets: a) two fingers captured by the same scanner; b) the same finger captured by two different scanners; and c) two fingers both captured by two different scanners. As a test data set we use GUC100 multi-scanner fingerprint database which contains fingerprint images of all ten fingers from 100 subjects using a number of different fingerprint scanners. In total 780 fusion scenarios are studied. Our analysis indicate that score level fusion using average rule provides improvement in all scenarios. The fusion of the same fingers from different scanners appears to provide more performance improvement compared to the fusion of different fingers from the same scanner. Furthermore, we also reveal that fusion of different fingers both collected by different scanners are the best. Interestingly, results suggest that for fusion differences in scanner type are more valuable than differences in finger type.