Fingerprint geometric hashing based on binary minutiae cylinder codes

Yi WANG, Lipeng Wang, Yiu Ming CHEUNG, Pong Chi YUEN

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

7 Citations (Scopus)

Abstract

Identity management has become increasingly more difficult with biometric big data. Hash-based indexing methods are promising for efficient searches in the high-dimensional space. Geometric hashing is one of the popular methods and has seen many of its variants proposed in the literature for fingerprint identification. Most of them use the same real-valued measures of local geometric invariants for both index creation and feature comparison. In this paper, we propose to build a 3D geometric hash table for storing binary minutiae cylinder codes with access keys that collectively describe the global geometric configuration. The proposed scheme is more robust against sample noise and distortion, and its most computation intensive part can be done efficiently in Hamming space. We perform fingerprint indexing experiments on the public benchmark databases of FVC2002 DB1 and NIST DB14. The results show that the performance of our approach can converge faster to high hit rates with lower penetration rates compared to other hash-based fingerprint indexing methods.

Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
PublisherIEEE
Pages690-695
Number of pages6
ISBN (Electronic)9781479952083
DOIs
Publication statusPublished - 4 Dec 2014
Event22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, Sweden
Duration: 24 Aug 201428 Aug 2014

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Conference

Conference22nd International Conference on Pattern Recognition, ICPR 2014
Country/TerritorySweden
CityStockholm
Period24/08/1428/08/14

Scopus Subject Areas

  • Computer Vision and Pattern Recognition

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