Feature Guided Fingerprint Pore Matching

Feng Liu, Yuanhao Zhao, Linlin Shen

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)


The huge number of sweat pores in fingerprint images results in low efficiency of direct pore (DP) matching methods. To overcome this drawback, this paper proposes a feature guided fingerprint pore matching method. It selects “distinctive” pores around the minutiae and singular points from fingerprint images which extremely reduced the number of pore features for matching. And then, the selected “distinctive” pores are matched using the-state-of-the-art DP matching methods. We also consider to take the select “distinctive” pores together with the extracted minutiae and singular points as a whole feature set for matching. The experimental results have shown that the matching time of the proposed method can be reduced to a quarter of the original time when the recognition accuracy is kept at the same level. Both of the matching time and recognition accuracy are improved when multi-features are taken as a whole set for matching.

Original languageEnglish
Title of host publicationBiometric Recognition - 12th Chinese Conference, CCBR 2017, Proceedings
EditorsYunhong Wang, Yu Qiao, Jie Zhou, Jianjiang Feng, Zhenan Sun, Zhenhua Guo, Shiguang Shan, Linlin Shen, Shiqi Yu, Yong Xu
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319699226
Publication statusPublished - 2017
Externally publishedYes
Event12th Chinese Conference on Biometric Recognition, CCBR 2017 - Beijing, China
Duration: 28 Oct 201729 Oct 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10568 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th Chinese Conference on Biometric Recognition, CCBR 2017


  • Fingerprint recognition
  • Pore matching
  • “Distinctive” pores

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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