  In this work, we study the protection that fuzzy commitments offer when they
are applied to facial images, processed by the state of the art deep learning
facial recognition systems. We show that while these systems are capable of
producing great accuracy, they produce templates of too little entropy. As a
result, we present a reconstruction attack that takes a protected template, and
reconstructs a facial image. The reconstructed facial images greatly resemble
the original ones. In the simplest attack scenario, more than 78% of these
reconstructed templates succeed in unlocking an account (when the system is
configured to 0.1% FAR). Even in the "hardest" settings (in which we take a
reconstructed image from one system and use it in a different system, with
different feature extraction process) the reconstructed image offers 50 to 120
times higher success rates than the system's FAR.
