Evolving the memory of a criminals face: methods to search a face space more effectively
Witnesses and victims of serious crime are often required to construct a facial composite from their memory, a visual likeness of a suspects 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 Principal Components Analysis. 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 witnesss 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.
face generation evolution face perception pca genetic algorithms