  Convolutional neural networks have made remarkable progress in the face
recognition field. The more the technology of face recognition advances, the
greater discriminative features into a face template. However, this increases
the threat to user privacy in case the template is exposed.
  In this paper, we present a modular architecture for face template
protection, called IronMask, that can be combined with any face recognition
system using angular distance metric. We circumvent the need for binarization,
which is the main cause of performance degradation in most existing face
template protections, by proposing a new real-valued error-correcting-code that
is compatible with real-valued templates and can therefore, minimize
performance degradation. We evaluate the efficacy of IronMask by extensive
experiments on two face recognitions, ArcFace and CosFace with three datasets,
CMU-Multi-PIE, FEI, and Color-FERET. According to our experimental results,
IronMask achieves a true accept rate (TAR) of 99.79% at a false accept rate
(FAR) of 0.0005% when combined with ArcFace, and 95.78% TAR at 0% FAR with
CosFace, while providing at least 115-bit security against known attacks.
