Multibiometrics for face recognition

Raymond N.J. Veldhuis; Farzin Deravi; Q. Tao

doi:10.1007/s11623-008-0033-8

Multibiometrics for face recognition

Raymond N.J. Veldhuis, Farzin Deravi, Q. Tao

Research output: Contribution to journal › Article › Academic › peer-review

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Abstract

Fusion is a popular practice to combine multiple sources of biometric information to achieve systems with greater performance and flexibility. In this paper various approaches to fusion within a multibiometrics context are considered and an application to the fusion of 2D and 3D face information is discussed. An optimal method for fusing the accept/reject decisions of individual biometric sources by means of simple logical rules is presented. Experimental results on the FRGC 2D and 3D face data show that the proposed technique performs effectively without the need for score normalization.

Original language	Undefined
Article number	10.1007/s11623-008-0033-8
Pages (from-to)	204-214
Number of pages	11
Journal	Datenschutz und Datensicherheit
Volume	32
Issue number	3
DOIs	https://doi.org/10.1007/s11623-008-0033-8
Publication status	Published - Mar 2008

Keywords

METIS-255940
CR-I.5
IR-62236
EWI-12178
SCS-Safety
EC Grant Agreement nr.: FP6/026845

Access to Document

10.1007/s11623-008-0033-8

http://eprints.eemcs.utwente.nl/secure2/12178/01/DuD2008_v4.pdf

Cite this

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title = "Multibiometrics for face recognition",

abstract = "Fusion is a popular practice to combine multiple sources of biometric information to achieve systems with greater performance and flexibility. In this paper various approaches to fusion within a multibiometrics context are considered and an application to the fusion of 2D and 3D face information is discussed. An optimal method for fusing the accept/reject decisions of individual biometric sources by means of simple logical rules is presented. Experimental results on the FRGC 2D and 3D face data show that the proposed technique performs effectively without the need for score normalization.",

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AU - Veldhuis, Raymond N.J.

AU - Deravi, Farzin

AU - Tao, Q.

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N2 - Fusion is a popular practice to combine multiple sources of biometric information to achieve systems with greater performance and flexibility. In this paper various approaches to fusion within a multibiometrics context are considered and an application to the fusion of 2D and 3D face information is discussed. An optimal method for fusing the accept/reject decisions of individual biometric sources by means of simple logical rules is presented. Experimental results on the FRGC 2D and 3D face data show that the proposed technique performs effectively without the need for score normalization.

AB - Fusion is a popular practice to combine multiple sources of biometric information to achieve systems with greater performance and flexibility. In this paper various approaches to fusion within a multibiometrics context are considered and an application to the fusion of 2D and 3D face information is discussed. An optimal method for fusing the accept/reject decisions of individual biometric sources by means of simple logical rules is presented. Experimental results on the FRGC 2D and 3D face data show that the proposed technique performs effectively without the need for score normalization.

KW - METIS-255940

KW - CR-I.5

KW - IR-62236

KW - EWI-12178

KW - SCS-Safety

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