Random subspace for an improved BioHashing for face authentication

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”.