  Faces manifest large variations in many aspects, such as identity,
expression, pose, and face styling. Therefore, it is a great challenge to
disentangle and extract these characteristics from facial images, especially in
an unsupervised manner. In this work, we introduce cycle-consistency in facial
characteristics as free supervisory signal to learn facial representations from
unlabeled facial images. The learning is realized by superimposing the facial
motion cycle-consistency and identity cycle-consistency constraints. The main
idea of the facial motion cycle-consistency is that, given a face with
expression, we can perform de-expression to a neutral face via the removal of
facial motion and further perform re-expression to reconstruct back to the
original face. The main idea of the identity cycle-consistency is to exploit
both de-identity into mean face by depriving the given neutral face of its
identity via feature re-normalization and re-identity into neutral face by
adding the personal attributes to the mean face. At training time, our model
learns to disentangle two distinct facial representations to be useful for
performing cycle-consistent face reconstruction. At test time, we use the
linear protocol scheme for evaluating facial representations on various tasks,
including facial expression recognition and head pose regression. We also can
directly apply the learnt facial representations to person recognition,
frontalization and image-to-image translation. Our experiments show that the
results of our approach is competitive with those of existing methods,
demonstrating the rich and unique information embedded in the disentangled
representations. Code is available at https://github.com/JiaRenChang/FaceCycle .
