Improving target identification using pairs of composite faces constructed by the same person

Abstract

Facial composites produced using traditional feature-based systems are notoriously hard to recognise. We have been developing a new more recognition-based system called EvoFIT that is performing better than other computerised approaches. In the current work, potential ways of improving performance even further were explored. It was found that asking the same person to construct two composites of a target face was successful in improving target identification. The data also found that composites constructed second were as identifiable as those constructed first, suggesting that the system does not appear to be interfering with a user’s memory of a target face. The work also indicated that switching from a monochrome to a colour face model produced a slight decrement in performance. Lastly, the work replicated a previous finding that constructing a composite of a distinctive face produces a more identifiable rendition than a composite of a more average-looking face.