High resolution partial fingerprint alignment using pore-valley descriptors

Qijun Zhao, Dapeng Zhang, Lei Zhang, Nan Luo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

96 Citations (Scopus)


This paper discusses the alignment of high resolution partial fingerprints, which is a crucial step in partial fingerprint recognition. The previously developed fingerprint alignment methods, including minutia-based and non-minutia feature based ones, are unsuitable for partial fingerprints because small fingerprint fragments often do not have enough features required by these methods. In this paper, we propose a new approach to aligning high resolution partial fingerprints based on pores, a type of fingerprint fine ridge features that are abundant on even small fingerprint areas. Pores are first extracted from the fingerprint images by using a difference of Gaussian filtering approach. After pore detection, a novel pore-valley descriptor (PVD) is proposed to characterize pores based on their locations and orientations, as well as the ridge orientation fields and valley structures around them. A PVD-based coarse-to-fine pore matching algorithm is then developed to locate pore correspondences. Once the corresponding pores are determined, the alignment transformation between two partial fingerprints can be estimated. The proposed method is compared with representative minutia based and orientation field based methods using the established high resolution partial fingerprint dataset and two fingerprint matchers. The experimental results show that the PVD-based method can more accurately locate corresponding feature points, estimate the alignment transformations, and hence significantly improve the accuracy of high resolution partial fingerprint recognition.
Original languageEnglish
Pages (from-to)1050-1061
Number of pages12
JournalPattern Recognition
Issue number3
Publication statusPublished - 1 Mar 2010


  • Fingerprint alignment
  • High resolution fingerprints
  • Partial fingerprints
  • Pores

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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