Modern face recognition systems, based on deep learning, convert face
images into highly representative features called “embeddings”. The
possibility of ‘inverting’ an embedding to recover the original face
image is already being explored (with promising results), which
represents a threat to the privacy of face recognition system users and
the security of the systems themselves. So, the aim of this project is
to investigate effective strategies for mitigating these threats by
converting face embeddings into a non-invertible, renewable
representation, thereby protecting the originals. The CITeR project
PolyProtect will extend on a method that we initially designed for
securing i-vectors in speaker recognition systems, PolyProtect, which is
based on multivariate polynomials applied to real-number vectors. Our
focus will be verification (1-to-1) systems only.