Study on novel Curvature Features for 3D fingerprint recognition

Feng Liu, Dapeng Zhang, Linlin Shen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

48 Citations (Scopus)

Abstract

The human finger is a three-dimensional object. More information will be provided if 3D fingerprint images are available compared with 2D fingerprints. This paper explores 3D fingerprint features, as well as their possible applications. Novel fingerprint features, which are defined as Curvature Features (e.g. curve-skeleton, overall maximum curvatures), are for the first time proposed and investigated in this paper. Those features are then employed to assist more accurate fingerprint matching or classify human gender after analyzing their characteristics. A series of experiments are conducted to evaluate the effectiveness of employing these novel fingerprint features to fingerprint recognition based on the established database with 541 fingers. Results show that an Equal error Rate (EER) of ~15% can be achieved when only curve-skeleton is used for recognition. But, promising EER of ~3.4% is realized by combining curve-skeleton with classical 2D fingerprint features for recognition that indicates the prospect of 3D fingerprint recognition. The proposed overall maximum curvatures are found to be helpful for human gender classification.
Original languageEnglish
Pages (from-to)599-608
Number of pages10
JournalNeurocomputing
Volume168
DOIs
Publication statusPublished - 1 Jan 2015

Keywords

  • Curvature fingerprint features
  • Curve-skeleton
  • Gender classification
  • Overall maximum curvatures
  • Touchless fingerprint recognition

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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