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).

Craniofacial superimposition is a forensic identification method involving the overlay of a skull over the available ante-mortem photographs of a candidate missing person face and the subsequent analysis of their anatomical correspondence. Within this process, the decision making stage focuses on determining the degree of support of being the same person or not based on the analysis of some criteria assessing the skull-face morphological correspondence. That decision is usually made in a non automatic and subjective way. We aim to automate the decision making process using computer vision and soft computing methods to assist the forensic anthropologist. In a previous study we have developed several methods to measure the matching of the correspondence between the face and the skull. The accuracy of each method was calculated as its capability to discriminate in a cross-comparison identification scenario. By the use of aggregation functions we can combine the results of the different methods taking into account the corresponding individual accuracy. This allows us to provide a single global output specifying the matching of each criterion while combining the capability of different methods. In this work, we present a study of the behavior of different aggregation functions for this aim. The performance of the aggregated methods has been tested on 172 skull-face overlay problem instances of positive and negative cases. The obtained results show that Sugeno integral ranks better than the counterparts although not significant conclusions can be delivered regarding the performance.

Craniofacial superimposition involves the process of overlaying a skull with a number of ante-mortem images of an individual and the analysis of their morphological correspondence. Within the craniofacial superimposition process, the skull-face overlay stage focuses on achieving the best possible overlay of the skull and a single ante-mortem image of a missing person. This technique has been commonly applied following a nonautomatic trial-and-error approach. Automatic skull-face overlay methods have been developed obtaining promising results. In this paper, we present two new variants that are an extension of existing 3-D-2-D methods to automatically superimpose a skull 3-D model on a facial photograph. We have modeled the imprecision related to the facial soft tissue depth between corresponding pairs of cranial and facial landmarks which typically guide the automatic approaches. As an illustration of the model's performance, the soft tissue distances associated to studies for Mediterranean population have been considered for dealing with this landmark matching uncertainty. Hence, we directly incorporate the corresponding landmark spatial relationships within the automatic skull-face overlay procedure. We have tested the performance of our proposal on 18 skull-face overlay instances from a ground truth data set obtaining valuable results. The current proposal is thus the first automatic skull-face overlay method evaluated in a reliable and unbiased way.

This paper introduces a new system for people localization in indoor environments. It is mainly based on an intelligent classifier able to distinguish among a set of predefined and well identified topological locations. We adopt a WiFi signal strength fingerprint approach where most effort is done during the training phase, while online execution is fast and effective. Our proposal has been tested in a real environment with data collected in five different experimental sessions. Achieved results are encouraging since they overcome those ones provided by the well-known nearest neighbour fingerprint matching algorithm, that is usually considered as baseline for WiFi localization.