  Biometric technologies, especially face recognition, have become an essential
part of identity management systems worldwide. In deployments of biometrics,
secure storage of biometric information is necessary in order to protect the
users' privacy. In this context, biometric cryptosystems are designed to meet
key requirements of biometric information protection enabling a
privacy-preserving storage and comparison of biometric data.
  This work investigates the application of a well-known biometric
cryptosystem, i.e. the improved fuzzy vault scheme, to facial feature vectors
extracted through deep convolutional neural networks. To this end, a feature
transformation method is introduced which maps fixed-length real-valued deep
feature vectors to integer-valued feature sets. As part of said feature
transformation, a detailed analysis of different feature quantisation and
binarisation techniques is conducted. 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. In experiments, an unlinkable improved deep face
fuzzy vault-based template protection scheme is constructed employing features
extracted with a state-of-the-art deep convolutional neural network trained
with the additive angular margin loss (ArcFace). 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
an effective face-based fuzzy vault scheme providing privacy protection of
facial reference data as well as digital key derivation from face.
