A Robust and Efficient Method for Skull-Face Overlay in Computerized Craniofacial Superimposition

Abstract

Craniofacial superimposition aims to identify a missing person by comparing its skull with photos of possible candidates. Among the difficult tasks involved, this requires superimposing the skull over each photo, matching the pose of the skull with that of the face, a problem known as skull-face overlay (SFO). Several computerized methods for SFO have been proposed, in an effort to relieve forensic experts of this complex, time-consuming, and subjective task. A system to generate artificial SFO data from computed tomography images has been also introduced, providing researchers with data to test and compare their techniques more reliably. This paper improves the state of the art on both fronts. We introduce a novel SFO algorithm that is substantially more accurate, more reliable, and much faster than the existing methods. An extensive experimental study and statistical analysis validates our findings. Moreover, we propose an improved method to simulate SFO data which, by replacing real photos with simulated ones, is able to generate a wider range of scenarios. This module provides complete control over the pose of the subject and the camera parameters, and even the ability to reproduce inter-expert errors in processing the input data, leading to a more controlled and thorough testing under realistic conditions.