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”.
Nanni, L., Lumini, A. (2008). Random Subspace for an improved BioHashing for Face authentication. PATTERN RECOGNITION LETTERS, 29, 295-300 [10.1016/j.patrec.2007.10.005].
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”.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.