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


  • Nicola Zaltron IT University of Copenhagen
  • Luisa Zurlo IT University of Copenhagen
  • Sebastian Risi IT University of Copenhagen



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.




How to Cite

Zaltron, N., Zurlo, L., & Risi, S. (2020). CG-GAN: An Interactive Evolutionary GAN-Based Approach for Facial Composite Generation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(03), 2544-2551.



AAAI Technical Track: Human-AI Collaboration