  Face frontalization is the process of synthesizing frontal facing views of
faces given its angled poses. We implement a generative adversarial network
(GAN) with spherical linear interpolation (Slerp) for frontalization of
unconstrained facial images. Our special focus is intended towards the
generation of approximate frontal faces of the side posed images captured from
surveillance cameras. Specifically, the present work is a comprehensive study
on the implementation of an auto-encoder based Boundary Equilibrium GAN (BEGAN)
to generate frontal faces using an interpolation of a side view face and its
mirrored view. To increase the quality of the interpolated output we implement
a BEGAN with Slerp. This approach could produce a promising output along with a
faster and more stable training for the model. The BEGAN model additionally has
a balanced generator-discriminator combination, which prevents mode collapse
along with a global convergence measure. It is expected that such an
approximate face generation model would be able to replace face composites used
in surveillance and crime detection.
