  The human face is one of the most crucial parts in interhuman communication.
Even when parts of the face are hidden or obstructed the underlying facial
movements can be understood. Machine learning approaches often fail in that
regard due to the complexity of the facial structures. To alleviate this
problem a common approach is to fine-tune a model for such a specific
application. However, this is computational intensive and might have to be
repeated for each desired analysis task. In this paper, we propose to
reconstruct obstructed facial parts to avoid the task of repeated fine-tuning.
As a result, existing facial analysis methods can be used without further
changes with respect to the data. In our approach, the restoration of facial
features is interpreted as a style transfer task between different recording
setups. By using the CycleGAN architecture the requirement of matched pairs,
which is often hard to fullfill, can be eliminated. To proof the viability of
our approach, we compare our reconstructions with real unobstructed recordings.
We created a novel data set in which 36 test subjects were recorded both with
and without 62 surface electromyography sensors attached to their faces. In our
evaluation, we feature typical facial analysis tasks, like the computation of
Facial Action Units and the detection of emotions. To further assess the
quality of the restoration, we also compare perceptional distances. We can
show, that scores similar to the videos without obstructing sensors can be
achieved.
