  Deep convolutional neural networks have achieved remarkable improvements in
facial recognition performance. Similar kinds of developments, e.g.
deconvolutional neural networks, have shown impressive results for
reconstructing face images from their corresponding embeddings in the latent
space. This poses a severe security risk which necessitates the protection of
stored deep face embeddings in order to prevent from misuse, e.g. identity
fraud.
  In this work, an unlinkable improved deep face fuzzy vault-based template
protection scheme is presented. To this end, a feature transformation method is
introduced which maps fixed-length real-valued deep face embeddings to
integer-valued feature sets. As part of said feature transformation, a detailed
analysis of different feature quantisation and binarisation techniques is
conducted using features extracted with a state-of-the-art deep convolutional
neural network trained with the additive angular margin loss (ArcFace). At key
binding, obtained feature sets are locked in an unlinkable improved fuzzy
vault. For key retrieval, the efficiency of different polynomial reconstruction
techniques is investigated. The proposed feature transformation method and
template protection scheme are agnostic of the biometric characteristic and,
thus, can be applied to virtually any biometric features computed by a deep
neural network.
  For the best configuration, a false non-match rate below 1% at a false match
rate of 0.01%, is achieved in cross-database experiments on the FERET and
FRGCv2 face databases. On average, a security level of up to approximately 28
bits is obtained. This work presents the first effective face-based fuzzy vault
scheme providing privacy protection of facial reference data as well as digital
key derivation from face.
