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Segmentation and enhancement of low quality fingerprint images
Dalarna University, School of Technology and Business Studies, Computer Engineering.ORCID iD: 0000-0002-1429-2345
2016 (English)In: Web Information Systems Engineering – WISE 2016: 17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings, Part II, China - Shanghai: Springer, 2016, Vol. 10042, 371-384 p.Conference paper, (Refereed)
Abstract [en]

This paper presents a new approach to segment low quality finger-print images which are collected by low quality fingerprint scanners. Images collected using such readers are easy to collect but difficult to segment. The proposed approach focuses on automatically segment and enhance these fingerprint images to reduce the detection of false minutiae and hence improve the recognition rate. There are four major contributions of this paper. Firstly, segmentation of fingerprint images is achieved via morphological filters to find the largest object in the image which is the foreground of the fingerprint. Secondly, specially designed adaptive thresholding algorithm to deal with fingerprint images. The algorithm tries to fit a curve between the gray levels of the pixels of each row or column in the fingerprint image. The curve represents the binarization threshold of each pixel in the corresponding row or column. Thirdly, noise reduction and ridge enhancement is achieved by invoking a rotational invariant anisotropic diffusion filter. Finally, an adaptive thinning algorithm which is immune against spurs is invoked to generate the recognition ready fingerprint image. Segmentation of 100 images from databases FVC2002 and FVC2004 was performed and the experiments showed that 96 % of images under test are correctly segmented.

Place, publisher, year, edition, pages
China - Shanghai: Springer, 2016. Vol. 10042, 371-384 p.
Series
Lecture Notes in Computer Science, E-ISSN 1611-3349
Keyword [en]
Unstructured data, Fingerprints, segmentation
National Category
Computer Systems
Research subject
Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-23409DOI: 10.1007/978-3-319-48743-4_30ISI: 000389505500030ISBN: 978-3-319-48742-7 (print)ISBN: 978-3-319-48743-4 (print)OAI: oai:DiVA.org:du-23409DiVA: diva2:1047927
Conference
17th International Conference on Web Information Systems Engineering (WISE)
Available from: 2016-11-19 Created: 2016-11-19 Last updated: 2017-03-30Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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