  This paper proposes PolyProtect, a method for protecting the sensitive face
embeddings that are used to represent people's faces in neural-network-based
face verification systems. PolyProtect transforms a face embedding to a more
secure template, using a mapping based on multivariate polynomials
parameterised by user-specific coefficients and exponents. In this work,
PolyProtect is evaluated on two open-source face recognition systems in a
cooperative-user mobile face verification context, under the toughest threat
model that assumes a fully-informed attacker with complete knowledge of the
system and all its parameters. Results indicate that PolyProtect can be tuned
to achieve a satisfactory trade-off between the recognition accuracy of the
PolyProtected face verification system and the irreversibility of the
PolyProtected templates. Furthermore, PolyProtected templates are shown to be
effectively unlinkable, especially if the user-specific parameters employed in
the PolyProtect mapping are selected in a non-naive manner. The evaluation is
conducted using practical methodologies with tangible results, to present
realistic insight into the method's robustness as a face embedding protection
scheme in practice. This work is fully reproducible using the publicly
available code at: https://gitlab.idiap.ch/bob/bob.paper.polyprotect_2021.
