Fingerprint pore extraction using convolutional neural networks and logical operation

Yuanhao Zhao, Feng Liu, Linlin Shen

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

4 Citations (Scopus)


Sweat pores have been proved to be discriminative and successfully used for automatic fingerprint recognition. It is crucial to extract pores precisely to achieve high recognition accuracy. To extract pores accurately and robustly, we propose a novel coarse-to-fine detection method based on convolutional neural networks (CNN) and logical operation. More specifically, pore candidates are coarsely estimated using logical operation at first; then, coarse pore candidates are further judged through well-trained CNN models; precise pore locations are finally refined by logical and morphological operation. The experimental results evaluated on the public dataset show that the proposed method outperforms other state-of-the-art methods in comparison.

Original languageEnglish
Title of host publicationBiometric Recognition - 13th Chinese Conference, CCBR 2018, Proceedings
EditorsZhenan Sun, Shiguang Shan, Zhenhong Jia, Kurban Ubul, Jie Zhou, Jianjiang Feng, Zhenhua Guo, Yunhong Wang
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319979083
Publication statusPublished - 2018
Externally publishedYes
Event13th Chinese Conference on Biometric Recognition, CCBR 2018 - Urumchi, China
Duration: 11 Aug 201812 Aug 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10996 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th Chinese Conference on Biometric Recognition, CCBR 2018


  • Convolutional neural network
  • Logical operation
  • Pore extraction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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