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Fingerprint Indexing Based on Combination of Novel Minutiae Triplet Features

  • Wei Zhou
  • Jiankun Hu
  • Song Wang
  • Ian Petersen
  • Mohammed Bennamoun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8792)

Abstract

Fingerprint indexing is a process of pre-filtering the template database before matching. The most common features used for fingerprint indexing are based on minutiae triplets. In this paper, we investigated the indexing performance based on some commonly used features of minutiae triplets and proposed to combine these features with some novel features of minutiae triplets for fingerprint indexing. Experiments on FVC 2000 DB2a and 2002 DB1a show that the proposed indexing method can perform better than state-of-the-art schemes for full fingerprint indexing, meanwhile, experimental results on NIST SD 14 show that the performance is improved significantly after the new features are added to the feature space, and is fairly good even for partial fingerprint indexing.

Keywords

Query Image Penetration Rate Indexing Scheme Query Sample Keystroke Dynamic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Wei Zhou
    • 1
  • Jiankun Hu
    • 1
  • Song Wang
    • 2
  • Ian Petersen
    • 1
  • Mohammed Bennamoun
    • 3
  1. 1.School of Engineering and Information TechnologyThe University of New South WalesCanberraAustralia
  2. 2.School of Engineering and Mathematical SciencesLaTrobe UniversityMelbourneAustralia
  3. 3.School of Computer Science and Software EngineeringThe University of Western AustraliaPerthAustralia

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