Interactive Evolution of Facial Composites

Zahradníková, Barbora; Šútova, Zuzana; Schreiber, Peter

doi:10.1016/j.ifacol.2017.08.1987

Cited by 2 publications

(1 citation statement)

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“…Simple systems merge single features (drawn or picked from a feature set) into a portrait. Others let the witnesses concentrate on the entire face, by selecting individuals as in a lineup (Zahradnikova, Sutova, and Schreiber 2017). This holistic approach is the result of psychological research suggesting that describing individual features causes lower identification rates (Zahradnikova, Sutova, and Schreiber 2017).…”

Section: Introductionmentioning

confidence: 99%

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

Zaltron

Zurlo

Risi

2020

AAAI

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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.

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Section: Introductionmentioning

confidence: 99%