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Symmetry Assessment by Finite Expansion: Application to forensic fingerprints
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
2014 (English)In: 2014 International Conference of the Biometrics Special Interest Group (BIOSIG) / [ed] Arslan Brömme & Christoph Busch, Bonn: Gesellschaft für Informatik, 2014, 87-98 p.Conference paper, Published paper (Refereed)
Abstract [en]

Common image features have too poor information for identification of forensic images of fingerprints, where only a small area of the finger is imaged and hence a small amount of key points are available. Noise, nonlinear deformation, and unknown rotation are additional issues that complicate identification of forensic fingerprints. We propose a feature extraction method which describes image information around key points: Symmetry Assessment by Finite Expansion (SAFE). The feature set has built-in quality estimates as well as a rotation invariance property. The theory is developed for continuous space, allowing compensation for features directly in the feature space when images undergo such rotation without actually rotating them. Experiments supporting that use of these features improves identification of forensic fingerprint images of the public NIST SD27 database are presented. Performance of matching orientation information in a neighborhood of core points has an EER of 24% with these features alone, without using minutiae constellations, in contrast to 36% when using minutiae alone. Rank-20 CMC is 58%, which is lower than 67% when using notably more manually collected minutiae information.

Place, publisher, year, edition, pages
Bonn: Gesellschaft für Informatik, 2014. 87-98 p.
Series
Lecture Notes in Informatics (LNI) - Proceedings, ISSN 1617-5468 ; P-230
Keyword [en]
forensic fingerprints, NIST SD27, orientation map, feature extraction, latent, structure tensor, biometrics
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-26583Scopus ID: 2-s2.0-84919337963Libris ID: 18243935ISBN: 978-3-88579-624-4 OAI: oai:DiVA.org:hh-26583DiVA: diva2:749719
Conference
13th International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, 10-12 September, 2014
Available from: 2014-09-25 Created: 2014-09-25 Last updated: 2015-09-11Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf