A supervised correlation analysis for score-level calibration of cross-device fingerprint recognition

Fangqing Gu, Yi Wang, Yiu Ming Cheung

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

1 Citation (Scopus)

Abstract

As the usage of fingerprint systems is rolled out on a large scale, scenarios have cross-device matching to allow information exchange and provide compatibility to the existing systems. A score-level calibration for device interoperability will require normalizing scores obtained from different devices so that they can be matched meaningfully and effectively. Conventional methods either assume a homogeneous distribution or model score distribution based on assumptions that may not be valid. In this paper, we circumvent the problem by leveraging correlations among the scores and propose a novel method for biometric score normalization. Our experiments show the promising results.

Original languageEnglish
Title of host publicationProceedings of 2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014
PublisherIEEE
Pages1165-1170
Number of pages6
Volume2014-January
EditionJanuary
ISBN (Electronic)9781479938407
DOIs
Publication statusPublished - Oct 2014
Event2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014 - San Diego, United States
Duration: 5 Oct 20148 Oct 2014

Publication series

NameProceedings of IEEE International Conference on Systems, Man, and Cybernetics, SMC
ISSN (Print)1062-922X

Conference

Conference2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014
Country/TerritoryUnited States
CitySan Diego
Period5/10/148/10/14

Scopus Subject Areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

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