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Machine learning techniques in biometrics

Prof. Alessandro Verri
Universit di Genova, Italy

In this talk the learning from examples problem is presented within the framework of Regularization Networks. The important notion of Reproducing Kernel Hilbert Space is briefly reviewed. We then show that within this framework several learning methods can be easily obtained. In particular we derive Support Vector Methods and discuss their basic mathematical properties: existence, uniqueness, and consistency. We then illustrate methods for tuning the SVM parameters and in particular for selecting the regularization parameter. The main computational issues behind the implementation of SVMs are presented and, finally, some experimental results in biometric applications are described.

 

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