  Facial composites are graphical representations of an eyewitness's memory of
a face. Many digital systems are available for the creation of such composites
but are either unable to reproduce features unless previously designed or do
not allow holistic changes to the image. In this paper, we improve the
efficiency of composite creation by removing the reliance on expert knowledge
and letting the system learn to represent faces from examples. The novel
approach, Composite Generating GAN (CG-GAN), applies generative and
evolutionary computation to allow casual users to easily create facial
composites. Specifically, CG-GAN utilizes the generator network of a pg-GAN to
create high-resolution human faces. Users are provided with several functions
to interactively breed and edit faces. CG-GAN offers a novel way of generating
and handling static and animated photo-realistic facial composites, with the
possibility of combining multiple representations of the same perpetrator,
generated by different eyewitnesses.
