  Face occlusions, covering either the majority or discriminative parts of the
face, can break facial perception and produce a drastic loss of information.
Biometric systems such as recent deep face recognition models are not immune to
obstructions or other objects covering parts of the face. While most of the
current face recognition methods are not optimized to handle occlusions, there
have been a few attempts to improve robustness directly in the training stage.
Unlike those, we propose to study the effect of generative face completion on
the recognition. We offer a face completion encoder-decoder, based on a
convolutional operator with a gating mechanism, trained with an ample set of
face occlusions. To systematically evaluate the impact of realistic occlusions
on recognition, we propose to play the occlusion game: we render 3D objects
onto different face parts, providing precious knowledge of what the impact is
of effectively removing those occlusions. Extensive experiments on the Labeled
Faces in the Wild (LFW), and its more difficult variant LFW-BLUFR, testify that
face completion is able to partially restore face perception in machine vision
systems for improved recognition.
