Oscar Ibáñez

Forensic identification by computer-aided craniofacial superimposition: A survey

Craniofacial superimposition is a forensic process in which a photograph of a missing person is compared with a skull found to determine its identity. After one century of development, craniofacial superimposition has become an interdisciplinary research field where computer sciences have acquired a key role as a complement of forensic sciences. Moreover, the availability of new digital equipment (such as computers and 3D scanners) has resulted in a significant advance in the applicability of this forensic identification technique. The purpose of this contribution is twofold. On the one hand, we aim to clearly define the different stages involved in the computer-aided craniofacial superimposition process. Besides, we aim to clarify the role played by computers in the methods considered. In order to accomplish these objectives, an up-to-date review of the recent works is presented along with a discussion of advantages and drawbacks of the existing approaches, with an emphasis on the automatic ones. Future case studies will be easily categorized by identifying which stage is tackled and which kind of computer-aided approach is chosen to face the identification problem. Remaining challenges are indicated and some directions for future research are given.

A cooperative coevolutionary approach dealing with the skull–face overlay uncertainty in forensic identification by craniofacial superimposition

Craniofacial superimposition 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 three-dimensional skull model against the face photo/video shot), the forensic anthropologist can try to establish whether that 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 previous work, we categorized the different sources of uncertainty and introduced the use of imprecise landmarks to tackle most of them. In this paper, we propose a novel approach, a cooperative coevolutionary algorithm, to deal with the use of imprecise cephalometric landmarks in the skull–face overlay process, the main task in craniofacial superimposition. Following this approach we are able to look for both the best projection parameters and the best landmark locations at the same time. Coevolutionary skull–face overlay results are compared with our previous fuzzy-evolutionary automatic method. Six skull–face overlay problem instances corresponding to three real-world cases solved by the Physical Anthropology Lab at the University of Granada (Spain) are considered. Promising results have been achieved, dramatically reducing the run time while improving the accuracy and robustness.

Past, present, and future of craniofacial superimposition: Literature and international surveys.

In this manuscript, the past, present and future of the identification of human remains based on craniofacial superimposition is reviewed. An analysis of the different technological approaches developed over time is offered in conjunction with a new classification based on the technology implemented throughout the diverse phases of the process. The state of the art of the technique, in the academic and forensic realms, is reflected in an extensive international survey that includes over one hundred experts worldwide. The results of the survey indicate the current relative importance of the technique, despite of its controversial nature within the scientific community. Finally, the future challenges to be faced to justify the use of this technique for either profiling, exclusion or identification purposes are discussed.

Ground truth data generation for skull–face overlay

Objective and unbiased validation studies over a significant number of cases are required to get a more solid picture on craniofacial superimposition reliability. It will not be possible to compare the performance of existing and upcoming methods for craniofacial superimposition without a common forensic database available for the research community. Skull–face overlay is a key task within craniofacial superimposition that has a direct influence on the subsequent task devoted to evaluate the skull–face relationships. In this work, we present the procedure to create for the first time such a dataset. We have also created a database with 19 skull–face overlay cases for which we are trying to overcome legal issues that allow us to make it public. The quantitative analysis made in the segmentation and registration stages, together with the visual assessment of the 19 face-to-face overlays, allows us to conclude that the results can be considered as a gold standard. With such a ground truth dataset, a new horizon is opened for the development of new automatic methods whose performance could be now objectively measured and compared against previous and future proposals. Additionally, other uses are expected to be explored to better understand the visual evaluation process of craniofacial relationships in craniofacial identification. It could be very useful also as a starting point for further studies on the prediction of the resulting facial morphology after corrective or reconstructive interventionism in maxillofacial surgery.

Image Segmentation Using Extended Topological Active Nets Optimized by Scatter Search

Image segmentation is the critical task of partitioning an image into multiple objects. Deformable Models are effective tools aimed at performing image segmentation. Among them, Topological Active Nets (TANs), and their extension, ETANs, are models integrating features of region-based and boundary-based segmentation techniques. Since the deformation of the meshes composing these models to fit the objects to be segmented is controlled by an energy functional, the segmentation task is tackled as a numerical optimization problem. Despite their good performance, the existing ETAN optimization method (based on a local search) can lead to result inaccuracies, that is, local optima in the sense of optimization. This paper introduces a novel optimization approach by embedding ETANs in a global search memetic framework, Scatter Search, thus considering multiple alternatives in the segmentation process using a very small solution population. With the aim of improving the accuracy of the segmentation results in a reasonable processing time, we introduce a global search-suitable internal energy term, a diversity function, a frequency memory population generator and two proper solution combination operators. In particular, these operators are effective in coalescing multiple meshes, a task previous global search methods for TAN optimization failed to accomplish. The proposal has been tested on a mix of 20 synthetic and real medical images with different segmentation difficulties. Its performance has been compared with two ETAN optimization approaches (the original local search and a new multi-start local search) as well as with the state-of-the-art memetic proposal for classical TAN optimization based on differential evolution. Our new method significantly outperformed the other three for the given set of images in terms of four standard segmentation metrics.

Computer vision and soft computing for automatic skull-face overlay in craniofacial superimposition

Craniofacial superimposition can provide evidence to support that some human skeletal remains belong or not to a missing person. It 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 just focuses on achieving the best possible overlay of the skull and a single ante mortem image of the suspect. Although craniofacial superimposition has been in use for over a century, skull-face overlay is still applied by means of a trial-and-error approach without an automatic method. Practitioners finish the process once they consider that a good enough overlay has been attained. Hence, skull-face overlay is a very challenging, subjective, error prone, and time consuming part of the whole process. Though the numerical assessment of the method quality has not been achieved yet, computer vision and soft computing arise as powerful tools to automate it, dramatically reducing the time taken by the expert and obtaining an unbiased overlay result. In this manuscript, we justify and analyze the use of these techniques to properly model the skull-face overlay problem. We also present the automatic technical procedure we have developed using these computational methods and show the four overlays obtained in two craniofacial superimposition cases. This automatic procedure can be thus considered as a tool to aid forensic anthropologists to develop the skull-face overlay, automating and avoiding subjectivity of the most tedious task within craniofacial superimposition.

Tackling the coplanarity problem in 3D camera calibration by means of fuzzy landmarks: a performance study in forensic craniofacial superimposition

Photographic supra-projection is a forensic process that aims to identify a missing person from a photograph and a skull found. Craniofacial superimposition is the second stage of this complex forensic technique devoted to find the most appropriate pose of the skull 3D model to be projected onto the photograph. Craniofacial superimposition can be modeled as a camera calibration problem in computer vision. The process is guided by a number of landmarks identified both in the skull (craniometric landmarks) and in the face (cephalometric landmarks). In this contribution we extend a previous work by studying the influence of the landmark selection procedure in the final superimposition result. In particular, we show how the coplanar landmarks do not carry discriminating depth information in our problem. Moreover, this fact negatively affects the superimposition results. In order to tackle that undesirable situation we propose the use of fuzzy landmarks that could extend the number of original landmarks identified by the forensic expert. Experiments using synthetic and real cases are considered.

Study on the performance of different craniofacial superimposition approaches (II): Best practices proposal

Craniofacial superimposition, although existing for one century, is still a controversial technique within the scientific community. Objective and unbiased validation studies over a significant number of cases are required to establish a more solid picture on the reliability. However, there is lack of protocols and standards in the application of the technique leading to contradictory information concerning reliability. Instead of following a uniform methodology, every expert tends to apply his own approach to the problem, based on the available technology and deep knowledge on human craniofacial anatomy, soft tissues, and their relationships. The aim of this study was to assess the reliability of different craniofacial superimposition methodologies and the corresponding technical approaches to this type of identification. With all the data generated, some of the most representative experts in craniofacial identification joined in a discussion intended to identify and agree on the most important issues that have to be considered to properly employ the craniofacial superimposition technique. As a consequence, the consortium has produced the current manuscript, which can be considered the first standard in the field; including good and bad practices, sources of error and uncertainties, technological requirements and desirable features, and finally a common scale for the craniofacial matching evaluation. Such a document is intended to be part of a more complete framework for craniofacial superimposition, to be developed during the FP7-founded project MEPROCS, which will favour and standardize its proper application.

Modeling Facial Soft Tissue Thickness for Automatic Skull-Face Overlay

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 models 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 Based on Genetic Algorithms and Fuzzy Location of Cephalometric Landmarks

Craniofacial superimposition is the second stage of a complex forensic technique that aims to identify a missing person from a photograph (or video shot) and the skull found. This specific task is devoted to find the most appropriate pose of the skull to be projected onto the photograph. The process is guided by a number of landmarks identified both in the skull (craniometric landmarks) and in the photograph (cephalometric landmarks). In this contribution we extend our previous genetic algorithm-based approach to the problem by considering the uncertainty involved in the location of the cephalometric landmarks. This proposal is tested over two real cases solved by the Physical Anthropology lab at the University of Granada (Spain).