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

Fangqing Gu; Yi WANG; Yiu Ming CHEUNG

doi:10.1109/SMC.2014.6974071

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

Fangqing Gu, Yi WANG, Yiu Ming CHEUNG

Research output: Contribution to journal › Conference article › peer-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 language	English
Article number	6974071
Pages (from-to)	1165-1170
Number of pages	6
Journal	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume	2014-January
Issue number	January
DOIs	https://doi.org/10.1109/SMC.2014.6974071
Publication status	Published - 2014
Event	2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014 - San Diego, United States Duration: 5 Oct 2014 → 8 Oct 2014

Scopus Subject Areas

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

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10.1109/SMC.2014.6974071

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title = "A supervised correlation analysis for score-level calibration of cross-device fingerprint recognition",

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.",

author = "Fangqing Gu and Yi WANG and CHEUNG, {Yiu Ming}",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE. Copyright: Copyright 2015 Elsevier B.V., All rights reserved.; 2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014 ; Conference date: 05-10-2014 Through 08-10-2014",

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doi = "10.1109/SMC.2014.6974071",

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A supervised correlation analysis for score-level calibration of cross-device fingerprint recognition. / Gu, Fangqing; WANG, Yi; CHEUNG, Yiu Ming.

In: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, Vol. 2014-January, No. January, 6974071, 2014, p. 1165-1170.

Research output: Contribution to journal › Conference article › peer-review

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N2 - 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.

AB - 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.

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JO - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

JF - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

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T2 - 2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014

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

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