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.