  Current research on soft-biometrics showed that privacy-sensitive information
can be deduced from biometric templates of an individual. Since for many
applications, these templates are expected to be used for recognition purposes
only, this raises major privacy issues. Previous works focused on supervised
privacy-enhancing solutions that require privacy-sensitive information about
individuals and limit their application to the suppression of single and
pre-defined attributes. Consequently, they do not take into account attributes
that are not considered in the training. In this work, we present Negative Face
Recognition (NFR), a novel face recognition approach that enhances the
soft-biometric privacy on the template-level by representing face templates in
a complementary (negative) domain. While ordinary templates characterize facial
properties of an individual, negative templates describe facial properties that
does not exist for this individual. This suppresses privacy-sensitive
information from stored templates. Experiments are conducted on two publicly
available datasets captured under controlled and uncontrolled scenarios on
three privacy-sensitive attributes. The experiments demonstrate that our
proposed approach reaches higher suppression rates than previous work, while
maintaining higher recognition performances as well. Unlike previous works, our
approach does not require privacy-sensitive labels and offers a more
comprehensive privacy-protection not limited to pre-defined attributes.
