The ability to determine the identity of a skull found at a crime scene is of critical importance to the law enforcement community. Traditional clay-based methods attempt to reconstruct the face so as to enable identification of the deceased by members of the general public. However, these reconstructions lack consistency from practitioner to practitioner and it has been shown that the human recognition of these reconstructions against a photo gallery of potential victims is little better than chance. In this talk we discuss the automation of the reconstruction process. For a given skull, a data-driven 3D generative model of the face is constructed using a database of CT head scans. The reconstruction can be constrained based on prior knowledge such as age and or weight. To determine whether or not these reconstructions have merit, geometric methods for comparing reconstructions against a gallery of facial images are proposed. First, active appearance models are used to automatically detect a set of facial landmarks on each image. These landmarks are associated with 3D points on the reconstruction. Direct comparison of the reconstruction is problematic since in general the camera geometry used for image capture is unknown and there are uncertainties associated with the reconstruction and landmark detection processes. The first method of comparison uses constrained optimization to determine the optimal projection of the reconstruction on to the image. Residuals are then analyzed resulting in a ranking of the gallery. The second method uses boosting to learn which points are both reliable and discriminating. This results in a match/no-match classifier. Experimental evidence indicating that skull recognition from facial images can be achieved is presented.