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

A study on the use of Boundary Equilibrium GAN for Approximate Frontalization of Unconstrained Faces to aid in Surveillance