Fusion, User-Specific

Fiérrez, Julián; Ortega‐Garcia, Javier

doi:10.1007/978-0-387-73003-5_161

Encyclopedia of Biometrics 2009

DOI: 10.1007/978-0-387-73003-5_161

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Fusion, User-Specific

Julián Fiérrez

Javier Ortega‐Garcia

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2012

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Improving Biometric Verification Systems by Fusing Z-norm and F-norm

Bengherabi

Harizi

Poh

et al. 2012

Lecture Notes in Computer Science

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Abstract. User-specific score normalization which is related to biometric menagerie has received a lot of attention in the last decade. It is a one-to-one mapping function such that after its application, only a global threshold is needed. In this paper we propose a novel user-specific score normalization framework based on the fusion of Z-norm and F-norm. In this framework, firstly one post-processes the biometric system scores with Z-norm and F-norm procedures. Then, one feeds the resulting two dimensional normalized score vector to a fusion classifier to obtain a final normalized score. Using logistic regression as a fusion classifier, experiments carried out on 13 face and speech systems of the XM2VTS database show that the proposed strategy is likely to improve over the original separate score normalization schemes (F-norm or Z-norm). Furthermore, this novel strategy turns out to be the best strategy for applications requiring low false acceptance rate.

show abstract

Improving Biometric Verification Systems by Fusing Z-norm and F-norm

Bengherabi

Harizi

Poh

et al. 2012

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

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Fusion, User-Specific

Cited by 1 publication

References 10 publications

Improving Biometric Verification Systems by Fusing Z-norm and F-norm

Improving Biometric Verification Systems by Fusing Z-norm and F-norm

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