Evolving the face of a criminal: how to search a face space more effectively

Abstract

Witnesses and victims of serious crime are often required to construct a facial composite, a visual likeness of a suspect’s face. The traditional method is for them to select individual facial features to build a face, but often these images are of poor quality. We have developed a new method whereby witnesses repeatedly select instances from an array of complete faces and a composite is evolved over time by searching a face model built using PCA. While past research suggests that the new approach is superior, performance is far from ideal. In the current research, face models are built which match a witness’s description of a target. It is found that such 'tailored' models promote better quality composites, presumably due to a more effective search, and also that smaller models may be even better. The work has implications for researchers who are using statistical modelling techniques for recognising faces.