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Multibiometrics: Information Fusion in Biometrics

Prof. Arun Ross
Michigan State University, USA

Multibiometrics refers to the consolidation of multiple sources of biometric evidence in order to establish the identity of an individual. These sources of information may correspond to different biometric traits (e.g., fingerprint + voice), sensors (e.g., 2D + 3D face cameras), feature extraction and matching techniques (e.g., minutiae-based + texture-based fingerprint matchers), or instances (e.g., left and right iris).

In this lecture, we will introduce various techniques that have been proposed in the literature to perform information fusion at the data level, feature level, score level, rank level, and decision level.

Further, we will discuss concepts in user-specific fusion, dynamic fusion and sparse representation, where extensive training data is used to generate robust fusion rules.

We will also present indexing techniques that can be used to quickly locate an identity in a large multibiometric database.

Finally, we will discuss the fusion of soft biometric traits with primary biometric data in order to deduce identities in non-ideal unconstrained environments.

The goal of this lecture is to introduce the audience to various fusion methods and to discuss biometric and forensic applications that benefit from them.

 

CO-ORGANIZED BY

European Commission

EU Horizon 2020
Project IDENTITY


PARTNERS AND SPONSORS

 

IAPR
Technical Committee on Biometrics (TC4)

 

IEEE

“Eurasip”
European Association for Signal Processing

 

GIRPR

Morpho - Safran group

“Griaule

EAB European Association for Biometrics

 

Biometrics Institute

 

Biosecure

 

University of Sassari

 

Athena