Statistically Deformable Face Models for Cranio-Facial Reconstruction

Forensic facial reconstruction aims at estimating the facial outlook associated to an unknown skull specimen. Estimation is based on tabulated average values of soft tissue thicknesses measured at a sparse set of landmarks on the skull. Traditional \u27plastic\u27 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 facial surface template. However, the resulting reconstruction is biased by the specific choice of the template and no face specific regularization is present. We reduce the bias by using a flexible statistical model of a dense set of facial surface points combined with an associated sparse set of skull landmarks. The statistical model also provides a probability value, which can be used to regularize the reconstruction towards more plausible outlooks. The reconstruction is obtained by fitting the model skull landmarks to the corresponding landmarks indicated on a digital copy of the skull to be reconstructed. The fitting process alternates between changing the face-specific statistical model parameters and interpolating 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. This iterative statistical model based reconstruction process is shown by experiment to converge to a realistic reconstruction of the face, independent of the initial template

Forensic Facial Reconstruction from Skeletal Remains

The identity of a skull in forensic is of critical importance. Forensic facial reconstruction is the reproduction of the lost or unknown facial features of an individual. In this paper, we propose the automation of the reconstruction process. For a given skull, a data-driven 3D generative model of the face is constructed using a database of CT head scans. The reconstruction can be constrained based on prior knowledge of parameters such as bone thickness measurements, cranial landmark distance measurements and demographics (age, weight, height, and BMI). The CT scan slices are segmented and a 3D model skull of 2D slices is generated with the help of Marching Cubes Algorithm. The 66 Landmark points are then calculated using Active Shape Models and PCA algorithm and placed on the skull. These Landmark points act as references for tissue generation. The facial soft tissue thickness is measured and estimated at the 66 craniometric landmarks used in forensic facial reconstruction. The skin mesh is generated using Delaunay automatic triangulation method. The performance of this model is then measured using RSME technique. The aim of this study is to develop a combination of techniques and algorithms to give the most accurate and efficient results

Statistical 3D Cranio-Facial Models

Abstract In forensic science, 3D cranio facail reconstruction is used to reconstruct the face from a skull. This can be done by manual approaches or computer assisted methods. The proposed statistical model represents the relationship between the skull and the soft tissues and is inverted to reconstruct the unknown face from the known skull. It is a specific application of the missing or occulted data problem. Results are visually correct

3D Semi-Landmarks Based Statistical Face Reconstruction

The aim of craniofacial reconstruction is to estimate the shape of a face from the shape of the skull. Few works in computerized assisted facial reconstruction have been provided in the past, probably due to technical (poor machine performances and data availability) and theoretical (complexity) reasons. Therefore, the main published works consist in manual reconstructions. In this paper, an original approach proposes first to build a 3D statistical model of the set skull/face from 3D CT scans. Then, a reconstruction method is introduced in order to estimate, from this statistical model, the 3D facial shape of one subject from known skull data

Data Driven Dense 3D Facial Reconstruction From 3D Skull Shape

Indiana University-Purdue University Indianapolis (IUPUI)This thesis explores a data driven machine learning based solution for Facial reconstruction from three dimensional (3D) skull shape for recognizing or identifying unknown subjects during forensic investigation. With over 8000 unidentified bodies during the past 3 decades, facial reconstruction of disintegrated bodies in helping with identification has been a critical issue for forensic practitioners. Historically, clay modelling has been used for facial reconstruction that not only requires an expert in the field but also demands a substantial amount of time for modelling, even after acquiring the skull model. Such manual reconstruction typically takes from a month to over 3 months of time and effort. The solution presented in this thesis uses 3D Cone Beam Computed Tomography (CBCT) data collected from many people to build a model of the relationship of facial skin to skull bone over a dense set of locations on the face. It then uses this skin-to-bone relationship model learned from the data to reconstruct the predicted face model from a skull shape of an unknown subject. The thesis also extends the algorithm in a way that could help modify the reconstructed face model interactively to account for the effects of age or weight. This uses the predicted face model as a starting point and creates different hypotheses of the facial appearances for different physical attributes. Attributes like age and body mass index (BMI) are used to show the physical facial appearance changes with the help of a tool we constructed. This could improve the identification process. The thesis also presents a methods designed for testing and validating the facial reconstruction algorithm

An automated computer-assisted approximation of the nose in South Africans from CBCT (Cone Beam Computed Tomography) scans

Thesis (PhD (Anatomy))--University of Pretoria, 2018.Each year in the Gauteng province of South Africa, approximately 1300 bodies are incinerated without a known identity (Bloom, 2015; Krüger et al., 2018). Because of various socio-economic reasons, identification is not always possible with conventional methods such as DNA comparisons and fingerprints. Therefore, more creative methods, including facial reconstruction, have been implemented to assist in the identification of unknown persons from their skeletal remains in South Africa. The aim of this thesis was to provide an automated computer-assisted method, independent of any forensic artistic interpretations, to create accurate statistical models for predicting nasal soft-tissue shape from information about the underlying skull substrate. The acquisition and extraction of the relevant anatomical structures (hard- and soft-tissue) were performed using an automatic dense landmarking procedure and analysed by geometric morphometrics. In this research, a validation of the precision of the automatic placement of landmarks, demonstrated its utilisation as a convenient prerequisite for geometric morphometric based shape analysis of the nasal complex. The automatic landmark positioning on hard- and soft-tissue 3D surfaces offered increased objectivity and the possibility of standardisation. In addition to reducing measurement errors in landmark placements, automatic landmarking, achieved a better precision for facial approximation, enabling the possibility to include more samples and populations with ease. A detailed study of the influence of factors (ancestry, sex, ageing and allometry) on the variability of the mid-facial skeleton among two South African ancestral groups were performed, revealing their statistically significant influences on the overall shape variation of the nose. Ancestry was found to be a very important factor in shape variation within the sample emphasising ancestral-specific differences. In addition, the expression of sexual dimorphism and effect of aging appeared to be different on distinct elements of the shape of the mid-facial region. From the findings, the two South African groups differed significantly regarding hard- and soft-tissue nasal complex morphology and their correlations, emphasising the importance of considering ancestry, sex and age as factors in the process of approximating the nose and highlighting the need for population specific accurate and reliable 3D statistical nose prediction methods. This study provided accurate statistical models using Partial Least Squared Regression (PLSR) algorithms which were optimised by including additional information such as ancestry, sex and age. Age and sex appeared to be important factors to be considered as additional information in order to improve the quality of the prediction. The predictions were based on a sample of 200 specimens resulting in an error when using the landmark-to-landmark distances on non-trained data, ranging between 2.139 mm and 2.833 mm for black South Africans at the tip of the nose and the alae, while they ranged from 2.575 mm to 2.859 mm for white South Africans. This research is the first attempt at a computer-assisted facial approximation of the nose with an automatic landmarking approach for the development of valid and reliable South African population specific standards using Cone Beam Computer-Tomography scans.AESOP + Erasmus Mundus ProgramAnatomyPhD (Anatomy)Unrestricte

Statistically deformable face models for cranio-facial reconstruction

Forensic facial reconstruction aims at estimating the facial outlook associated to an unknown skull specimen. Estimation is 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 facial surface template. However, the resulting reconstruction is biased by the specific choice of the template. We reduce this bias by using a flexible statistical model of a dense set of facial surface points combined with an associated sparse set of skull landmarks. The reconstruction is obtained by fitting the model skull landmarks to the corresponding landmarks indicated on a digital copy of the skull to be reconstructed. The fitting process alternates between changing the facespecific statistical model parameters and interpolating the remaining landmark fit error using a minimal bending ThinPlate Spline (TPS) based deformation. This iterative process is shown by experiment to converge to a realistic reconstruction of the face, independent of the initial template.Claes P., Vandermeulen D., De Greef S., Willems G., Suetens P., ''Statistically deformable face models for cranio-facial reconstruction'', Proceedings 4th international symposium on image and signal processing and analysis - ISPA2005, pp. 347-352, September 15-17, 2005, Zagreb, Croatia.status: publishe

A Forensic Identification Utility to Create Facial Approximations using Cone-Beam Computed Tomography of 100 Hispanic Females: A Pilot Study

Introduction:Estimation of facial soft tissue appearance from human skeletal remains is often necessary in forensic identification. This process has been referred to as facial reconstruction or facial approximation and is a branch of forensic facial anthropology. Original methods for facial approximation originated in nineteenth century Europe and consisted of artists shaping clay over skull models using average soft tissue depths measured in cadavers. The last two decades have introduced numerous computerized techniques that have digitized this process while attempting to accurately and objectively define the relationship between a skull and its overlying soft tissue. This pilot study describes a method of facial approximation that combines cephalometric techniques for characterization of the craniofacial complex commonly used in the field of orthodontics with a database of cone-beam computed tomography (CBCT) skull images. Facial likenesses for an unknown skull are automatically located within the database by comparing cephalometric values recorded on the unknown skull with those within the database. A recently proposed method of sex determination based on the anatomy of the mastoid process, glabellar process, and frontal sinus area is also applied to the sample used in this study. Methods:A database consisting of one-hundred (100) cone-beam computed tomography (CBCT) skull images of Hispanic female patients of the University of Las Vegas, Nevada School of Dental Medicine Orthodontic Department [age range 8 to 23 years (mean 13.5 years)] was constructed. A cephalometric analysis consisting of twelve (12) landmarks and nineteen (19) skull measurements [sixteen (16) angular and three (3) proportional] was defined and applied to all database entries. Facial approximations were created for three skulls by sequentially removing three (3) random entries from the database and treating these as unknown (leave-one-out cross validation). A weighted least-sum-of-squares (WLSS) regression algorithm was applied to measure the cephalometric similarity between each entry in the database and the unknown skull data to find the three (3) most cephalometrically similar skulls in the database (three closest matches). Accuracy was assessed through expert face pool resemblance ranking. Soft tissue profiles associated with the three best matches were grouped with three random database entries to create a face pool array of size six (6) for each unknown. Fourteen (14) post-doctoral orthodontic graduate students were utilized as expert face pool evaluators. Sex determination accuracy was then assessed by comparing the values of eight (8) cephalometric measurements taken on this sample to those already described and proven efficacious on other samples in the literature. Results:Intraexaminer reliability was acceptable for all cephalometric measurements. Expert face pool resemblance rankings results implied that the described process was able to select database entries that approximated the unknown face better than random database entries. In Face Pools One, Two, and Three the three highest ranked faces contained two, two, and three algorithm-selected faces, respectively. Sex determination data recorded on this sample was comparable to data described in the literature. Conclusions:Contemporary methods of facial approximation have shown that estimation of soft tissues from skeletal data can be achieved by employing computationally and graphically complex techniques. It now also seems plausible to rapidly estimate the general shape of an unidentified skull\u27s facial profile by comparison of the unknown skull\u27s cephalometric data to those in a database of orthodontic patients. Further research involving the described method is warranted