Craniofacial reconstruction using a combined statistical model of face shape and soft tissue depths: methodology and validation.

Forensic facial reconstruction aims at estimating the facial outlook associated with an unidentified skull specimen. Estimation is generally based on tabulated average values of soft tissue thicknesses measured at a sparse set of landmarks on the skull. Traditional 'plastic' methods apply modeling clay or plasticine on a cast of the skull, approximating the estimated tissue depths at the landmarks and interpolating in between. Current computerized techniques mimic this landmark interpolation procedure using a single static facial surface template. However, the resulting reconstruction is biased by the specific choice of the template and no face-specific regularization is used during the interpolation process. We reduce the template bias by using a flexible statistical model of a dense set of facial surface points, combined with an associated sparse set of skull-based landmarks. This statistical model is constructed from a facial database of (N = 118) individuals and limits the reconstructions to statistically plausible outlooks. The actual reconstruction is obtained by fitting the skull-based landmarks of the template model to the corresponding landmarks indicated on a digital copy of the skull to be reconstructed. The fitting process changes the face-specific statistical model parameters in a regularized way and interpolates the remaining landmark fit error using a minimal bending thin-plate spline (TPS)-based deformation. Furthermore, estimated properties of the skull specimen (BMI, age and gender, e.g.) can be incorporated as conditions on the reconstruction by removing property-related shape variation from the statistical model description before the fitting process. The proposed statistical method is validated, both in terms of accuracy and identification success rate, based on leave-one-out cross-validation tests applied on the facial database. Accuracy results are obtained by statistically analyzing the local 3D facial surface differences of the reconstructions and their corresponding ground truth. Identification success rate is obtained by comparing, based on correlation, Euclidean distance matrix (EDM) signatures of the reconstructed and the original 3D facial surfaces in the database. A subjective identification success rate is quantified based on face-pool tests. Finally a qualitative comparison is made between facial reconstructions of a real-case skull, based on two typical static face models and our statistical model, showing the shortcomings of current face models and the improved performance of the statistical model.

Computerized craniofacial reconstruction using CT-derived implicit surface representations.

In forensic craniofacial reconstruction, facial features of an unknown individual are estimated from an unidentified skull, based on a mixture of experimentally obtained guidelines on the relationship between soft tissues and the underlying skeleton. In this paper, we investigate the possibility of using full 3D cross-sectional CT images for establishing a reference database of densely sampled distances between the external surfaces of the skull and head for automated craniofacial reconstruction. For each CT image in the reference database, the hard tissue (skull) and soft tissue (head) volumes are automatically segmented and transformed into signed distance transform (sDT) images, representing for each voxel in this image the Euclidean distance to the closest point on the skull and head surface, respectively, distances being positive (negative) for voxels inside (outside) the skull/head. Multiple craniofacial reconstructions are obtained by first warping each reference skull sDT maps to the target skull sDT using a B-spline based free form deformation algorithm and subsequently applying these warps to the reference head sDT maps. A single reconstruction of the target head surface is defined as the zero level set of the arithmetic average of all warped reference head sDT maps, but other reconstructions are possible, biasing the result to subject specific attributes (age, BMI, gender). Both qualitative and quantitative tests (measuring the similarity between the 3D reconstructed and corresponding original head surface) on a small (N = 20) database are presented to proof the validity of the concept.

Three-dimensional cranio-facial reconstruction in forensic identification: latest progress and new tendencies in the 21st century.

Three-dimensional (3D) cranio-facial reconstruction can be useful in the identification of an unknown body. The progress in computer science and the improvement of medical imaging technologies during recent years had significant repercussions on this domain. New facial soft tissue depth data for children and adults have been obtained using ultrasound, CT-scans and radiographies. New guidelines for facial feature properties such as nose projection, eye protrusion or mouth width, have been suggested, but also older theories and "rules of thumbs" have been critically evaluated based on digital technology. New fast, flexible and objective 3D reconstruction computer programs are in full development. The research on craniofacial reconstruction since the beginning of the 21st century is presented, highlighting computer-aided 3D facial reconstruction. Employing the newer technologies and permanently evaluating and (re)questioning the obtained results will hopefully lead to more accurate reconstructions.

Semi-automated ultrasound facial soft tissue depth registration: method and validation.

A mobile and fast, semi-automatic ultrasound (US) system was developed for facial soft tissue depth registration. The system consists of an A-Scan ultrasound device connected to a portable PC with interfacing and controlling software. For 52 cephalometric landmarks, the system was tested for repeatability and accuracy by evaluating intra-observer agreement and comparing ultrasound and CT-scan results on 12 subjects planned for craniofacial surgery, respectively. A paired t-test evaluating repeatability of the ultrasound measurements showed 5.7% (n = 3) of the landmarks being significantly different (p < 0.01). US and CT-scan results showed significant differences (p < 0.01) using a Wilcoxon signed rank test analysis for 11.5% (n = 6) of the landmarks. This is attributed to a difference in the volunteer's head position between lying (CT) and sitting (US). Based on these tests, we conclude that the proposed registration system and measurement protocol allows relatively fast (52 landmarks/20 min), non-invasive, repeatable and accurate acquisition of facial soft tissue depth measurements.

A spatially-dense regression study of facial form and tissue depth: towards an interactive tool for craniofacial reconstruction.

Forensic Craniofacial Reconstruction (CFR) is an investigative technique used to illicit recognition of a deceased person by reconstructing the most likely face starting from the skull. A key component in most CFR methods are estimates of facial soft tissue depths (TD) at particular points (landmarks) on the skull based on averages from databases of TD recordings. These databases vary in their method of extraction, number and position of landmarks (usually sparse <100), condition of the body, population studied, and sub-categorization of the data. In this work a new dataset is presented in a novel manner based on 156 CT scans using a spatially-dense set (∼7500) of TD recordings to allow for a complete understanding of TD variation interpolating between typical landmarks. Furthermore, to unravel the interplay between soft-tissue layers, skull and facial morphology, TD and Facial Form (FF) are investigated both separately and combined. Using a partial least squares regression (PLSR) analysis, which allows for working with multivariate and spatially-dense data, on metadata of Sex, Age and BMI, different significant patterns on TD and FF variation were found. A similar, but with TD and FF combined, PLSR generated a model useful to report on both, in function of Sex, Age and BMI. In contrast to other datasets and due to the continuous nature of the regression there is no need for data sub-categorization. In further contrast, previous datasets have been presented in tabulated form, which is impractical for spatially-dense data. Instead an interactive tool was built to visualize the regression model in an accessible way for CFR practitioners as well as anatomists. The tool is free to the community and forms a base for data contributions to augment the model and its future use in practice.

Bayesian estimation of optimal craniofacial reconstructions.

Forensic craniofacial reconstruction (CFR) aims at estimating the facial outlook associated to an unknown skull for victim identification. Computerized CFR techniques are essentially a virtual mimicking of manual CFR techniques and all share the same conceptual model-based framework. We propose a fully automated Bayesian based statistical framework estimating the most probable face, according to a known craniofacial model (CFM), given the, possibly inaccurate, skull data. A multivariate Gaussian distribution is assumed for the shape parameters of the CFM, only allowing face-like solutions. The CFM is improved by encoding tissue depth differently as an extra value for 52 landmarks on the face and by incorporating gray-valued texture information. A fully automated and consistent technique is obtained by the use of an implicit target skull representation (TSR). The most plausible face associated to the target skull is calculated using an expectation-maximization procedure that is robust to small (noise) and/or gross errors (outliers). A clinical database of 12 individuals is used for simulating realistic reconstruction scenarios. Validation is performed in terms of reconstruction accuracy and recognition success. Within the same EM reconstruction framework, the proposed procedure is compared to alternative reconstructions using different target skull representations and different CFMs incorporating various amounts of covariance. The results indicate that the proposed CFM performs better than the other models. Furthermore, the use of the implicit TSR generates more consistent and better results compared to a realistic landmark based skull representation. Finally, these results also confirm that the Bayesian framework formulation is indeed robust against noise and outliers in the skull data.

Computerized craniofacial reconstruction: Conceptual framework and review.

When confronted with a corpse that is unrecognizable due to its state of decomposition, soft-tissue mutilation or incineration, and if no other identification evidence is available, craniofacial reconstruction (CFR) can be a useful tool in the identification of the body. Traditional methods are based on manual reconstruction by physically modelling a face on a skull replica with clay or plasticine. The progress in computer science and the improvement of medical imaging technologies during recent years has had a significant impact on this domain. New, fast, flexible and computer-based objective reconstruction programs are under development. Employing the newer technologies and permanently evaluating the obtained results will hopefully lead to more accurate reconstructions, beneficial to the added value of CFR methods during crime-scene investigations. A general model-based workflow is observed, when analysing computerized CFR techniques today. The main purpose of this paper is to give an overview of existing computer-based CFR methods up to date defined within a common framework using a general taxonomy. The paper will also discuss the various alternatives and problems which arise during the process of designing a CFR program.

[Computer-assisted facial reconstruction: recent developments and trends]. / Reconstitutions faciales assistées par ordinateur: développements récents et tendances.

Three-dimensional (3D) craniofacial reconstruction can be a useful tool in the identification of an unknown body. The progress in computer science and the improvement of medical imaging technologies during recent years has had a significant impact on this domain. New facial soft tissue depth data have been obtained. New guidelines for facial feature properties such as nose projection, eye protrusion or mouth width, have been suggested, but also older theories and "rules of thumbs" have been critically evaluated based on digital technology. New fast, flexible and objective 3D reconstruction computer-based programs are in full development. Employing the newer technologies and permanently evaluating the obtained results will hopefully lead to more accurate reconstructions.