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From angling smiles to duck faces, all kinds of facial expressions can be
seen in selfies, portraits, and Internet pictures. These photos are taken from
various camera types, and under a vast range of angles and lighting conditions.
We present a deep learning framework that can fully normalize
unconstrained face images, i.e., remove perspective distortions, relight to an
evenly lit environment, and predict a frontal and neutral face. Our method
can produce a high resolution image while preserving important facial details
and the likeness of the subject, along with the original background.
We divide this ill-posed problem into three consecutive normalization steps,
each using a different generative adversarial network that acts as an image
generator. Perspective distortion removal is performed using a dense flow
field predictor. A uniformly illuminated face is obtained using a lighting
translation network, and the facial expression is neutralized
using a generalized facial expression synthesis framework combined with a regression
network based on deep features for facial recognition. We introduce new
data representations for conditional inference, as well as training methods
for supervised learning to ensure that different expressions of the same
person can yield to not only a plausible but also a similar neutral face. We
demonstrate our results on a wide range of challenging images collected in
the wild. Key applications of our method range from robust image-based
3D avatar creation, portrait manipulation, to facial enhancement
and reconstruction tasks for crime investigation. We also found through an extensive
user study, that our normalization results can be hardly distinguished from
ground truth ones if the person is not familiar
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