The comparison of dental records is a standardized technique in forensic odontology used to speed up the identification of individuals in multiple-comparison scenarios. Specifically, the odontogram comparison is a procedure to compute criteria that will be used to perform a ranking. State-of-the-art automatic methods either make use of simple techniques, without utilizing the full potential of the information obtained from a comparison, or their internal behavior is not known due to the lack of detailed description or peer-reviewed publications. This work aims to design aggregation mechanisms to automatically compare pairs of dental records that can be understood and validated by experts, improving the current methods. To do so, we introduce different aggregation approaches using the state-of-the-art codification, based on seven different criteria. In particular, we study the performance of: (i) data-driven lexicographical order-based aggregations, (ii) well-known fuzzy logic aggregation methods, and (iii) machine learning techniques as aggregation mechanisms. To validate our proposals, 215 real-world forensic cases from two different populations have been used. The results obtained show how the use of white-box machine learning techniques as aggregation models (average ranking from 2.02 to 2.21) are able to improve the state of the art (average ranking of 3.91) without compromising the explainability and interpretability of the method.

Facial biometrics play an essential role in the fields of law enforcement and forensic sciences. When comparing facial traits for human identification in photographs or videos, the analysis must account for several factors that impair the application of common identification techniques, such as illumination, pose, or expression. In particular, facial attributes can drastically change depending on the distance between the subject and the camera at the time of the picture. This effect is known as perspective distortion, which can severely affect the outcome of the comparative analysis. Hence, knowing the subject-to-camera distance of the original scene where the photograph was taken can help determine the degree of distortion, improve the accuracy of computer-aided recognition tools, and increase the reliability of human identification and further analyses. In this paper, we propose a deep learning approach to estimate the subject-to-camera distance of facial photographs: FacialSCDnet. Furthermore, we introduce a novel evaluation metric designed to guide the learning process, based on changes in facial distortion at different distances. To validate our proposal, we collected a novel dataset of facial photographs taken at several distances using both synthetic and real data. Our approach is fully automatic and can provide a numerical distance estimation for up to six meters, beyond which changes in facial distortion are not significant. The proposed method achieves an accurate estimation, with an average error below 6 cm of subject-to-camera distance for facial photographs in any frontal or lateral head pose, robust to facial hair, glasses, and partial occlusion. • Accurate estimation of subject-to-camera distance in portrait photographs. • A novel metric is proposed, based on the effects of perspective in facial distortion. • A new database of facial images at a distance is introduced for human identification. • A transfer learning approach overcomes the limitations of current methods. • Robust to expression, occlusion and pose without requiring anatomical information.

Comparative radiography is the forensic anthropology technique in which ante-mortem (AM) and post-mortem (PM) radiographic materials (e.g., X-ray images or CTs) are compared in order to determine the identity of a deceased human being. One of the most commonly used anatomical structures in comparative radiography are the frontal sinuses. The frontal sinuses are osseous cavities located in the skull, which are used in forensic identification tasks due to their singularity and high identification power. In order to automate the comparison of frontal sinuses in AM and PM materials, it is necessary to perform the registration of these materials (i.e., it is necessary to carry out the alignment of these anatomical regions). However, the manual alignment of these structures is a time-consuming and subjective process. In order to tackle this problem, this paper presents an automatic frontal sinuses registration method in comparative radiography using real-coded evolutionary algorithms (RCEAs). The task is formulated as a 2D-3D image registration problem using a 9 Degrees of Freedom perspective transformation model; two RCEAs (DE and MVMOSH) are compared in the minimization of the registration cost function, and the best of them (MVMO-SH) is applied to an identification scenario including 50X-ray images and 50 CTs. The results obtained show that the proposed automatic identification system is able to filter more than 80% of the sample.

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.

Artificial Neuron-Glia Networks (ANGNs) are a novel bio-inspired machine learning approach. They extend classical Artificial Neural Networks (ANNs) by incorporating recent findings and suppositions about the way information is processed by neural and astrocytic networks in the most evolved living organisms. Although ANGNs are not a consolidated method, their performance against the traditional approach, i.e. without artificial astrocytes, was already demonstrated on classification problems. However, the corresponding learning algorithms developed so far strongly depends on a set of glial parameters which are manually tuned for each specific problem. As a consequence, previous experimental tests have to be done in order to determine an adequate set of values, making such manual parameter configuration time-consuming, error-prone, biased and problem dependent. Thus, in this paper, we propose a novel learning approach for ANGNs that fully automates the learning process, and gives the possibility of testing any kind of reasonable parameter configuration for each specific problem. This new learning algorithm, based on coevolutionary genetic algorithms, is able to properly learn all the ANGNs parameters. Its performance is tested on five classification problems achieving significantly better results than ANGN and competitive results with ANN approaches.

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.

Craniofacial superimposition (CS) is a forensic process where photographs or video shots of a missing person are compared with the skull that is found. By projecting both photographs on top of each other (or, even better, matching a scanned 3-D skull model against the face photo/video shot), the forensic anthropologist can try to establish whether it is the same person. The whole process is influenced by inherent uncertainty, mainly because two objects of different nature (a skull and a face) are involved. In this paper, we extend our previous evolutionary-algorithm-based method to automatically superimpose the 3-D skull model and the 2-D face photo with the aim to overcome the limitations that are associated with the different sources of uncertainty, which are present in the problem. Two different approaches to handle the imprecision will be proposed: weighted and fuzzy-set-theory-based landmarks. The performance of the new proposal is analyzed, considering five skull–face overlay problem instances that correspond to three real-world cases solved by the Physical Anthropology Laboratory, University of Granada, Granada, Spain. The experimental study that is developed shows how the fuzzy-set-based approach clearly outperforms the previous crisp solution. Finally, the proposed method is validated by the comparison of its outcomes with respect to those manually achieved by the forensic experts in nine skull–face overlay problem instances.