Craniofacial superimposition (CFS) is a forensic identification technique, which studies the anatomical and morphological correspondence between a skull and a face. It involves the process of overlaying a variable number of facial images with the skull. This technique has great potential, since nowadays a wide majority of people have photographs, where their faces are clearly visible. In addition, the skull is a bone that hardly degrades under the effect of fire, humidity, temperature changes, and so on. Three consecutive stages for the CFS process have been distinguished: the acquisition and processing of the materials, the skull-face overlay, and the decision making. This final stage consists of determining the degree of support for a match based on the previous overlays. The final decision is guided by different criteria depending on the anatomical relations between the skull and the face. In previous approaches, we proposed a framework for automating this stage at different levels, taking into consideration all the information and uncertainty sources involved. In this paper, we model new anatomical skull-face regions and tackle the last level of the hierarchical decision support system. For the first time, we present a complete system, which provides a final degree of craniofacial correspondence. Furthermore, we validate our system as an automatic identification tool analyzing its capabilities in closed (known information or a potential list of those involved) and open lists (little or no idea at first who may be involved) and comparing its performance with the manual results achieved by experts, obtaining a remarkable performance. The proposed system has been demonstrated to be valid for sort-listing a given data set of initial candidates (in 62.5% of the cases, the positive one is ranked in the first position) and to serve as an exclusion method (97.4% and 96% of true negatives in training and test, respectively).