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

Fangqing Gu, Yi Wang, Yiu Ming Cheung

Research output: Contribution to journalConference articlepeer-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
Article number6974071
Pages (from-to)1165-1170
Number of pages6
JournalConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume2014-January
Issue numberJanuary
DOIs
Publication statusPublished - 2014
Event2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014 - San Diego, United States
Duration: 5 Oct 20148 Oct 2014

Scopus Subject Areas

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

Fingerprint

Dive into the research topics of 'A supervised correlation analysis for score-level calibration of cross-device fingerprint recognition'. Together they form a unique fingerprint.

Cite this