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

Nicola Zaltron, Luisa Zurlo, Sebastian Risi

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


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.
Original languageUndefined/Unknown
Title of host publicationProceedings of the AAAI Conference on Artificial Intelligence
Number of pages8
PublisherAssociation for the Advancement of Artificial Intelligence
Publication date1 Apr 2020
Publication statusPublished - 1 Apr 2020
SeriesProceedings of the AAAI Conference on Artificial Intelligence

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