DEEPPOREID: An Effective Pore Representation Descriptor in Direct Pore Matching

Yuanhao Zhao, Guojie Liu, Feng Liu, Linlin Shen, Qin Li

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

2 Citations (Scopus)

Abstract

This paper proposes an effective pore representation descriptor based on Convolutional Neural Networks (CNNs). We make full use of the diversity and large quantities of sweat pores in fingerprints to learn a deep feature, denoted as DeepPoreID. The DeepPoreID is then used to describe the local feature for each pore and finally integrated into the classical direct pore matching method. Experiments carried on the challenge public high-resolution fingerprint database with small image size of 320 × 240 shows the effectiveness of the proposed DeepPoreID. The results also have shown that the proposed method outperforms other existing state-of-the-art methods in the aspect of recognition accuracy. About ~35% rise in accuracy can be obtained when compared with the best result achieved by existing methods.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PublisherIEEE Computer Society
Pages1690-1694
Number of pages5
ISBN (Electronic)9781538662496
DOIs
Publication statusPublished - Sep 2019
Externally publishedYes
Event26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China
Duration: 22 Sep 201925 Sep 2019

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2019-September
ISSN (Print)1522-4880

Conference

Conference26th IEEE International Conference on Image Processing, ICIP 2019
Country/TerritoryTaiwan, Province of China
CityTaipei
Period22/09/1925/09/19

Keywords

  • convolutional neural networks
  • direct pore matching
  • pore representation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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