A directional approach to fingerprint classification

Li Min Liu, Ching Yu Huang, D. C.Douglas Hung

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

In this article, we present a new fingerprint classification algorithm. Singular points are first extracted from enhanced fingerprint direction images with a resolution of 2 × 2 pixels by the modified SEA algorithm. Based on the number of singular points, fingerprints are categorized into types of "arch", "whorl", and "solitary". Solitary fingerprints are properly rotated and then further processed to generate direction patterns that lead to establishment of individual direction template. Direction constraints are formed and derived from pattern descriptors by their structural layout. Decision rules are then established and pattern templates are classified into three more types: "right loop", "left loop", and "tented arch". NIST-4 database was used for an experimental test, and our classification accuracy was 91.62% with 1.55% rejection on the five-class system (94.38% on the four-class system), which is the best result on the five-class system to-date. An additional experiment on NIST-14 database reports 89.15% accuracy with 3.07% rejection.

Original languageEnglish (US)
Pages (from-to)347-365
Number of pages19
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume22
Issue number2
DOIs
StatePublished - Mar 2008

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition

Keywords

  • Directional image
  • Fault line
  • Fingerprint classification
  • Singular points

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