Verification based on tokenised pseudo-random numbers and user specific biometric feature has received much attention. In this paper we propose a BioHashing system for automatic face recognition based on Fisher-Based Feature Transform, a supervised transform for dimensionality reduction that has been proved to be very effective for the face recognition task. Since the dimension of the Fisher-Based transformed space is bounded by the number of classes – 1, we use Random Subspace to create K feature spaces to be concatenated in a new higher dimensional space, in order to obtain a big and reliable “BioHash code”.

Random Subspace for an improved BioHashing for Face authentication

NANNI, LORIS;LUMINI, ALESSANDRA
2008

Abstract

Verification based on tokenised pseudo-random numbers and user specific biometric feature has received much attention. In this paper we propose a BioHashing system for automatic face recognition based on Fisher-Based Feature Transform, a supervised transform for dimensionality reduction that has been proved to be very effective for the face recognition task. Since the dimension of the Fisher-Based transformed space is bounded by the number of classes – 1, we use Random Subspace to create K feature spaces to be concatenated in a new higher dimensional space, in order to obtain a big and reliable “BioHash code”.
2008
Nanni, Loris; Lumini, Alessandra
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/54636
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