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Facial composites are graphical representations of an eyewitness's memory ofa face. Many digital systems are available for the creation of such compositesbut are either unable to reproduce features unless previously designed or donot allow holistic changes to the image. In this paper, we improve theefficiency of composite creation by removing the reliance on expert knowledgeand letting the system learn to represent faces from examples. The novelapproach, Composite Generating GAN (CG-GAN), applies generative andevolutionary computation to allow casual users to easily create facialcomposites. Specifically, CG-GAN utilizes the generator network of a pg-GAN tocreate high-resolution human faces. Users are provided with several functionsto interactively breed and edit faces. CG-GAN offers a novel way of generatingand handling static and animated photo-realistic facial composites, with thepossibility of combining multiple representations of the same perpetrator,generated by different eyewitnesses.

CG-GAN: An Interactive Evolutionary GAN-based Approach for Facial Composite Generation)