Automated fingerprint recognition using structural matching
Pattern Recognition
On-Line Fingerprint Verification
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining multiple matchers for a high security fingerprint verification system
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Online Fingerprint Template Improvement
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sum Versus Vote Fusion in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Biometrics: Personal Identification in Networked Society
Biometrics: Personal Identification in Networked Society
Filterbank-based fingerprint matching
IEEE Transactions on Image Processing
A model-based method for the computation of fingerprints' orientation field
IEEE Transactions on Image Processing
Fingerprint recognition using model-based density map
IEEE Transactions on Image Processing
Crease detection from fingerprint images and its applications in elderly people
Pattern Recognition
Binary spectral minutiae representation with multi-sample fusion for fingerprint recognition
Proceedings of the 12th ACM workshop on Multimedia and security
Rolled fingerprint construction using MRF-based nonrigid image registration
IEEE Transactions on Image Processing
A performance improvement method for existing fingerprint systems
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Multimodal fingerprint verification by score-level fusion: An experimental investigation
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In fingerprint verification systems, there are usually multiple (from two to four) enrolled impressions for a same finger. The performance of the systems can be improved by combining these impressions through feature fusion or decision fusion strategy. In this paper, different schemes to combine multiple enrolled impressions are comparatively studied. Experimental results show that a larger improvement can be obtained by using decision fusion scheme than feature fusion. In all decision fusion rules, sum rule outperforms voting rule a little whether using similarity or Neyman-Pearson rule. Based on the observation that the performance of these two strategies can complement each other, we also propose a novel fusion scheme to further combine feature fusion and decision fusion, which can produce an even better result.