Deep learning has attracted several researchers in the
field of computer vision due to its ability to perform face and
object recognition tasks with high accuracy than the traditional
shallow learning systems. The convolutional layers present in the
deep learning systems help to successfully capture the distinctive
features of the face. For biometric authentication, face
recognition (FR) has been preferred due to its passive nature.
Processing face images are accompanied by a series of
complexities, like variation of pose, light, face expression, and
make up. Although all aspects are important, the one that
impacts the most face-related computer vision applications is
pose. In face recognition, it has been long desired to have a
method capable of bringing faces to the same pose, usually a
frontal view, in order to ease recognition. Synthesizing different
views of a face is still a great challenge, mostly because in nonfrontal face images there are loss of information when one side
of the face occludes the other. Most solutions for FR fail to
perform well in cases involving extreme pose variations as in
such scenarios, the convolutional layers of the deep models are
unable to find discriminative parts of the face for extracting
information. Most of the architectures proposed earlier deal with
the scenarios where the face images used for training as well as
testing the deep learning models are frontal and nearfrontal. On
the contrary, here a limited number of face images at different
poses is used to train the model, where a number of separate
generator models learn to map a single face image at any
arbitrary pose to specific poses and the discriminator performs
the task of face recognition along with discriminating a synthetic
face from a realworld sample. To this end, this paper proposes a
representation learning by rotating the face. Here an encoderdecoder structure of the generator enables to learn a
representation that is both generative and discriminative, which
can be used for face image synthesis and pose-invariant face
recognition. This representation is explicitly disentangled from
other face variations such as pose, through the pose code
provided to the decoder and pose estimation in the discriminator.
Style-Based Unsupervised Learning for Real-World Face Image Super-Resolution
