Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
FVC2000: Fingerprint Verification Competition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Fingerprint Template Improvement
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Alignment of Fingerprint Images
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Combining Crypto with Biometrics Effectively
IEEE Transactions on Computers
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Fingerprint verification using spectral minutiae representations
IEEE Transactions on Information Forensics and Security
Spectral minutiae representations of fingerprints enhanced by quality data
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Binary Representations of Fingerprint Spectral Minutiae Features
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Practical biometric authentication with template protection
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Theoretical and Practical Boundaries of Binary Secure Sketches
IEEE Transactions on Information Forensics and Security
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Biometric fusion is the approach to improve the biometric system performance by combining multiple sources of biometric information. The binary spectral minutiae representation is a method to represent a fingerprint minutiae set as a fixed-length binary string. This binary representation has the advantages of a fast operation and a small template storage. It also enables the combination of a biometric system with template protection schemes that require a fixed-length feature vector as input. In this paper, based on the spectral minutiae representation algorithm, we investigate the multi-sample fusion algorithms at the feature-, score-, and decision-level respectively. Furthermore, we propose different schemes to mask out unreliable bits. The algorithms are evaluated on the FVC2000-DB2 database and showed promising results.