A local technique based on vectorized surfaces for craniofacial reconstruction

Automated reconstruction: Predictive models based on facial morphology matrices

Forensic Facial Approximation (FFA) has evolved, with techniques advancing to refine the intercorrelation between the soft-tissue facial profile and the underlying skull. FFA has become essential for identifying unknown persons in South Africa, where the high number of migrant and illegal labourers and many unidentified remains make the identification process challenging. However, existing FFA methods are based on American or European standards, rendering them inapplicable in a South African context. We addressed this issue by conducting a study to create prediction models based on the relationships between facial morphology and known factors, such as population affinity, sex, and age, in white South African and French samples. We retrospectively collected 184 adult cone beam computed tomography (CBCT) scans representing 76 white South Africans (29 males and 47 females) and 108 French nationals (54 males and 54 females) to develop predictive statistical models using a projection onto latent structures regression algorithm (PLSR). On training and untrained datasets, the accuracy of the estimated soft-tissue shape of the ears, eyes, nose, and mouth was measured using metric deviations. The predictive models were optimized by integrating additional variables such as sex and age. Based on trained data, the prediction errors for the ears, eyes, nose, and mouth ranged between 1.6 mm and 4.1 mm for white South Africans; for the French group, they ranged between 1.9 mm and 4.2 mm. Prediction errors on non-trained data ranged between 1.6 mm and 4.3 mm for white South Africans, whereas prediction errors ranging between 1.8 mm and 4.3 mm were observed for the French. Ultimately, our study provided promising predictive models. Although the statistical models can be improved, the inherent variability among individuals restricts the accuracy of FFA. The predictive validity of the models was improved by including sex and age variables and considering population affinity. By integrating these factors, more customized and accurate predictive models can be developed, ultimately strengthening the effectiveness of forensic analysis in the South African region.

Using Computed Tomography (CT) Data to Build 3D Resources for Forensic Craniofacial Identification

Forensic craniofacial identification encompasses the practices of forensic facial approximation (aka facial reconstruction) and craniofacial superimposition within the field of forensic art in the United States. Training in forensic facial approximation methods historically has used plaster copies, high-cost commercially molded skulls, and photographs. Despite the increased accessibility of computed tomography (CT) and the numerous studies utilizing CT data to better inform facial approximation methods, 3D CT data have not yet been widely used to produce interactive resources or reference catalogs aimed at forensic art practitioner use or method standardization. There are many free, open-source 3D software packages that allow engagement in immersive studies of the relationships between the craniofacial skeleton and facial features and facilitate collaboration between researchers and practitioners. 3D CT software, in particular, allows the bone and soft tissue to be visualized simultaneously with tools such as transparency, clipping, and volume rendering of underlying tissues, allowing for more accurate analyses of bone to soft tissue relationships. Analyses and visualization of 3D CT data can not only facilitate basic research into facial variation and anatomical relationships relevant for reconstructions but can also lead to improved facial reconstruction guidelines. Further, skull and face surface models exported in digital 3D formats allow for 3D printing of custom reference models and novel training materials and modalities for practitioners. This chapter outlines the 3D resources that can be built from CT data for forensic craniofacial identification methods, including how to view 3D craniofacial CT data and modify surface models for 3D printing.

Craniofacial Reconstruction Method Based on Region Fusion Strategy

Craniofacial reconstruction is to estimate a person's face model from the skull. It can be applied in many fields such as forensic medicine, archaeology, and face animation. Craniofacial reconstruction is based on the relationship between the skull and the face to reconstruct the facial appearance from the skull. However, the craniofacial structure is very complex and the relationship is not the same in different craniofacial regions. To better represent the shape changes of the skull and face and make better use of the correlation between different local regions, a new craniofacial reconstruction method based on region fusion strategy is proposed in this paper. This method has the flexibility of finding the nonlinear relationship between skull and face variables and is easy to solve. Firstly, the skull and face are divided into five corresponding local regions; secondly, the five regions of skull and face are mapped to low-dimensional latent space using Gaussian process latent variable model (GP-LVM), and the nonlinear features between skull and face are extracted; then, least square support vector regression (LSSVR) model is trained in latent space to establish the mapping relationship between skull region and face region; finally, perform regional fusion to achieve overall reconstruction. For the unknown skull, first divide the region, then project it into the latent space of the skull region, then use the trained LSSVR model to reconstruct the face of the corresponding region, and finally perform regional fusion to realize the face reconstruction of the unknown skull. The experimental results show that the method is effective. Compared with other regression methods, our method is optimal. In addition, we add attributes such as age and body mass index (BMI) to the mappings to achieve face reconstruction with different attributes.

Nose approximation among South African groups from cone-beam computed tomography (CBCT) using a new computer-assisted method based on automatic landmarking

Considering the high demand for the identification of unknown remains in South Africa, a need exists to establish reliable facial approximation techniques that will take into account sex and age and, most importantly, be useful within the South African context. This study aimed to provide accurate statistical models for predicting nasal soft-tissue shape from information about the underlying skull subtract among a South African sample. The database containing 200 cone-beam computer tomography (CBCT) scans (100 black South Africans and 100 white South Africans). The acquisition and extraction of the 3D relevant anatomical structures (hard- and soft-tissue) were performed by an automated three-dimensional (3D) method based on an automatic dense landmarking procedure using MeVisLab © v. 2.7.1 software. An evaluation of shape differences attributed to known factors (ancestry, sex, size, and age) was performed using geometric morphometric and statistical models of prediction were created using a Projection onto Latent Structures Regression (PLSR) algorithm. The accuracy of the estimated soft-tissue nose was evaluated in terms of metric deviations on training and un-trained datasets. Our findings demonstrated the influence of factors (sex, aging, and allometry) on the variability of the hard- and soft-tissue among two South African population groups. This research provides accurate statistical models optimized by including additional information such as ancestry, sex, and age. When using the landmark-to landmark distances, the prediction errors ranged between 1.769mm and 2.164mm for black South Africans at the tip of the nose and the alae, while they ranged from 2.068mm to 2.175mm for the white subsample. The prediction errors on un-trained data were slightly larger, ranging between 2.139mm and 2.833mm for the black South African sample at the tip of the nose and the alae and ranging from 2.575mm to 2.859mm for the white South African sample. This research demonstrates the utilization of an automated 3Dmethod based on an automatic landmarking method as a convenient prerequisite for providing a valid and reliable nose prediction model that meets population-specific standards for South Africans.

A facial reconstruction method based on new mesh deformation techniques

This article presents a new numerical method for facial reconstruction. The problem is the following: given a dry skull, reconstruct a virtual face that would help in the identification of the subject. The approach combines classical features as the use of a skulls/faces database and more original aspects: (1) an original shape matching method is used to link the unknown skull to the database templates; (2) the final face is seen as an elastic 3D mask that is deformed and adapted onto the unknown skull. In this method, the skull is considered as a whole surface and not restricted to some anatomical landmarks, allowing a dense description of the skull/face relationship. Also, the approach is fully automated. Various results are presented to show its efficiency.

Skeletal dimensions as predictors for the shape of the nose in a South African sample: A cone-beam computed tomography (CBCT) study

The profile of the nose is an important feature for facial approximations. Although several manual and semi-automated prediction guidelines exist for estimating the shape of the nose, the reliability and applicability of these methods to South Africans groups are unknown. The aim of this study was to predict the displacements of capulometric landmarks from hard-tissue planes to facilitate nasal soft-tissue reconstruction in a South African sample. Cone beam computed tomography (CBCT) scans of 120 adult South Africans were selected from the Oral and Dental Hospital, University of Pretoria, South Africa. Measurements involving craniometric and capulometric landmarks of the nose were obtained as plane-to-plane distances. Correlation coefficients between hard- and soft-tissue measurements were determined, and regression equations computed to assist in the prediction of the most probable shape and size of the nose. All hard- and soft-tissue measurements appeared significantly different between groups, except for the distance between the pronasale and nasion in the transverse plane and for the distance between the alare and the nasion in the coronal plane. The nasal height, nasal bone length and the nasal bone projection were significant predictors of the pronasale, subnasale and alare positions. More precisely, the nasal height and the nasal bone length were significant predictors of the pronasale position in both groups. Nasal bone projection was only useful for predicting shape in white South Africans. The variation in the skeletal predictors of the external shape of the nose noted between black and white South Africans and the results of the cross-validation testing emphasize the need for population specific guidelines.

A regional method for craniofacial reconstruction based on coordinate adjustments and a new fusion strategy

Craniofacial reconstruction recreates a facial outlook from the cranium based on the relationship between the face and the skull to assist identification. But craniofacial structures are very complex, and this relationship is not the same in different craniofacial regions. Several regional methods have recently been proposed, these methods segmented the face and skull into regions, and the relationship of each region is then learned independently, after that, facial regions for a given skull are estimated and finally glued together to generate a face. Most of these regional methods use vertex coordinates to represent the regions, and they define a uniform coordinate system for all of the regions. Consequently, the inconsistence in the positions of regions between different individuals is not eliminated before learning the relationships between the face and skull regions, and this reduces the accuracy of the craniofacial reconstruction. In order to solve this problem, an improved regional method is proposed in this paper involving two types of coordinate adjustments. One is the global coordinate adjustment performed on the skulls and faces with the purpose to eliminate the inconsistence of position and pose of the heads; the other is the local coordinate adjustment performed on the skull and face regions with the purpose to eliminate the inconsistence of position of these regions. After these two coordinate adjustments, partial least squares regression (PLSR) is used to estimate the relationship between the face region and the skull region. In order to obtain a more accurate reconstruction, a new fusion strategy is also proposed in the paper to maintain the reconstructed feature regions when gluing the facial regions together. This is based on the observation that the feature regions usually have less reconstruction errors compared to rest of the face. The results demonstrate that the coordinate adjustments and the new fusion strategy can significantly improve the craniofacial reconstructions.