Algorithms for clustering data
Algorithms for clustering data
On-Line Fingerprint Verification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Fingerprint Template Improvement
IEEE Transactions on Pattern Analysis and Machine Intelligence
FVC2002: Second Fingerprint Verification Competition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Minutiae-based template synthesis and matching for fingerprint authentication
Computer Vision and Image Understanding
Super-template generation using successive bayesian estimation for fingerprint enrollment
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
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A biometric authentication system operates by acquiring biometric data from a user and comparing it against the template data stored in a database in order to identify a person or to verify a claimed identity. Most systems store multiple templates per user to account for variations in a person's biometric data. In this paper we propose two techniques to automatically select prototype fingerprint templates for a finger from a given set of fingerprint impressions. The first method, called DEND, performs clustering in order to choose a template set that best represents the intra-class variations, while the second method, called MDIST, selects templates that have maximum similarity with the rest of the impressions and, therefore, represent typical measurements of biometric data. Matching results on a database of 50 different fingers, with 100 impressions per finger, indicate that a systematic template selection procedure as presented here results in better performance than random template selection.