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”.File in questo prodotto:
Eventuali allegati, non sono esposti
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.