## 4 Comparison of Classical and Computer-Aided Reconstruction Results in Case 1
### Discussion of the Comparison Using Computer Vision  
As mentioned above, when considering the reconstruction results of a forensic facial soft tissue reconstruction, it should not be assumed that the individual appearance of an un  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Image 1 dp**} & \multicolumn{2}{c}{**Image 2 dp**} \\ \hline
**Model** & **Cosine** & **Euclidean** & **Cosine** & **Euclidean** \\ \hline VGG-Face & 1.3027 & 1.1396 & 1.2759 & 1.1382 \\ GhostFaceNet & 1.2923 & 1.1664 & 1.4823 & 1.2457 \\ Dlib & n.a. & 1.2827 & n.a. & 1.2950 \\ \hline \hline \end{tabular} Note: n.a. = not applicable.  
\end{table}
Table 9: Normalized similarity scores for two test images (Image 1: frontal passport photo; Image 2: slightly lateral view) across three face recognition models. Both cosine similarity and Euclidean distance were evaluated. All scores are normalized such that a value of 1.0 corresponds to the respective model-specific decision threshold. Note that Dlib does not support cosine similarity; therefore, entries are marked as not applicable (n.a.). Smaller values correspond to higher degrees of facial similarity.  
known deceased person can be reconstructed in every detail. It is more of an approximation to the individual's appearance.  
The minimal differences in the normalized similarity scores between Image 1 (frontal passport photo) and Image 2 (slightly lateral view) suggest that the pose angle had little impact on the face recognition results. Despite the slight lateral angle in Image 2, the face remains largely frontal, meaning the models did not encounter significant pose variation. Both VGG-Face and GhostFaceNet are known for their robustness to small pose changes, which is reflected in the consistent results across both images.  
It is important to note that all the face recognition models used here were trained on large benchmark datasets that include a wide variety of individuals in diverse environments. These datasets typically feature significant variations in lighting, exposure, angles, facial expressions, and even occlusions [86], which makes the models highly robust to such factors. Consequently, these models perform well even when the images deviate slightly from the ideal frontal view, as seen in the comparison of Image 1 and Image 2.  
These findings indicate that the slight pose change did not significantly affect the results, though larger pose variations might yield different outcomes.  
Human perception seems capable of recognizing similarities even when deviations in facial features are noticeable upon closer examination. This ability was reflected in Case 2, where the police officers involved noted that the similarity to the reconstructed face was \"astonishing\" (translated from German), with one remarking, \"As a layperson, I see a significant match\" (translated from German). Additionally, the press reported, \"Indeed, based on comparison photos of the missing person [...], a resemblance to the reconstructed face was noticeable, the investigators reported\" (translated from German) [88]. However, facial recognition software is particularly sensitive to minor facial features and will reject matches if there are discrepancies. The ultimate goal is to enable the automatic and rapid search of large databases using facial recognition software based on soft tissue reconstructions. To achieve this, further research is needed to generate more accurate reconstructions.  
An additional approach for facial recognition could involve training models to compare a 3D-generated or reconstructed face with a photograph of the person. However, this approach is quite challenging due to the need for a large dataset containing both 3D models of individuals and corresponding photographs. A possible method would resemble the approach taken in the work of [PERSON] et al., which tackles the recognition of composite sketches and photographs using deep learning techniques [89].