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

Learning Facial Representations from the Cycle-consistency of Face