Partial fingerprint matching using minutiae and ridge shape features for small fingerprint scanners

Wonjune Lee, Sungchul Cho, Heeseung Choi, Jaihie Kim

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

Currently, most mobile devices adopt very small fingerprint sensors that only capture small partial fingerprint images. Accordingly, conventional minutiae-based fingerprint matchers are not capable of providing convincing results due to the insufficiency of minutiae. To secure diverse mobile applications such as those requiring privacy protection and mobile payments, a more accurate fingerprint matcher is demanded. This manuscript proposes a new partial fingerprint-matching method incorporating new ridge shape features (RSFs) in addition to the conventional minutia features. These new RSFs represent the small ridge segments where specific edge shapes (concave and convex) are observed, and they are detectable in conventional 500 dpi images. The RSFs are effectively utilized in the proposed matching scheme which consists of minutiae matching and ridge-feature-matching stages. In the minutiae matching stage, corresponding minutia pairs are determined by comparing the local RSFs and minutiae adjacent to each minutia. During the subsequent ridge-feature-matching stage, the RSFs in the overlapped area of two images are further compared to enhance the matching accuracy. A final matching score is obtained by combining the resulting scores from the two matching stages. Various tests for partial matching were conducted on the FVC2002, FVC2004 and BERC (self-constructed) databases, and the proposed method shows significantly lower equal-error rates compared to other matching methods. The results show that the proposed method improves the accuracy of fingerprint recognition, especially for implementation in mobile devices where small fingerprint scanners are adopted.

Original languageEnglish
Pages (from-to)183-198
Number of pages16
JournalExpert Systems with Applications
Volume87
DOIs
Publication statusPublished - 2017 Nov 30

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Mobile devices
Sensors

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Cite this

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abstract = "Currently, most mobile devices adopt very small fingerprint sensors that only capture small partial fingerprint images. Accordingly, conventional minutiae-based fingerprint matchers are not capable of providing convincing results due to the insufficiency of minutiae. To secure diverse mobile applications such as those requiring privacy protection and mobile payments, a more accurate fingerprint matcher is demanded. This manuscript proposes a new partial fingerprint-matching method incorporating new ridge shape features (RSFs) in addition to the conventional minutia features. These new RSFs represent the small ridge segments where specific edge shapes (concave and convex) are observed, and they are detectable in conventional 500 dpi images. The RSFs are effectively utilized in the proposed matching scheme which consists of minutiae matching and ridge-feature-matching stages. In the minutiae matching stage, corresponding minutia pairs are determined by comparing the local RSFs and minutiae adjacent to each minutia. During the subsequent ridge-feature-matching stage, the RSFs in the overlapped area of two images are further compared to enhance the matching accuracy. A final matching score is obtained by combining the resulting scores from the two matching stages. Various tests for partial matching were conducted on the FVC2002, FVC2004 and BERC (self-constructed) databases, and the proposed method shows significantly lower equal-error rates compared to other matching methods. The results show that the proposed method improves the accuracy of fingerprint recognition, especially for implementation in mobile devices where small fingerprint scanners are adopted.",
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Partial fingerprint matching using minutiae and ridge shape features for small fingerprint scanners. / Lee, Wonjune; Cho, Sungchul; Choi, Heeseung; Kim, Jaihie.

In: Expert Systems with Applications, Vol. 87, 30.11.2017, p. 183-198.

Research output: Contribution to journalArticle

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