## 4 Discussion  
Research in the forensic context is pushing for acceptance of the 3D data offered by the new technology, which must prove the strength of the evidence. Morphological features must be preserved, and accurate metric reconstruction is essential to maintain proportionality. These advances aim to increase the evidential value and acceptance of forensic facial  
Figure 21: The matching score of each frame obtained with the 2D mug shot (in orange) was compared with the best score obtained within the fifteen poses (in blue).  
Figure 20: The 12 selected frames of the subject.  
recognition technology and 3D mug shot within the legal system and, therefore, facial reconstruction for application in a forensic context requires more rigorous approaches. This study proposes a multi-view photogrammetric approach utilizing low-cost Raspberry Pi cameras to create high-precision 3D mugshots with submillimetre accuracy.  
We first designed the network in a virtual environment to verify the camera configuration, then we compared the outcomes obtained working in a world frame through GCPs distributed in the environments and in a relative frame. The results of the tests realized using the external orientation parameters set in the virtual environment compared with those obtained using estimated orientation parameters, highlight the critical influence of the external orientation task. It is more prone to error than the alignment in a relative frame. Our results indicate that cameras with reduced fields of view, due to their sensor dimensions, require three rows of cameras to produce a robust image bundle oriented in a relative frame. Tests realized in a real setting emphasize the importance of accurately estimating the scale factor whenever it is necessary to extract metric information from the face. Specifically, we observed that 80% of the points had a distance of less than \(\pm 1\) mm when multiple scale bars were used to estimate the scale factor. More specifically, we used 10 scale bars extracted from the GCPs behind the model.  
Furthermore, despite the limited number of synchronized cameras, we were able to obtain the 3D models from three volunteers, who were able to maintain their position for several minutes. More specifically, one of them was a complete model, which allowed us to evaluate the improvement in forensic recognition of the 3D mugshots over the 2D mugshots, showing an increase in matching score of up to 0.42 points, especially in scenarios where the subject is not captured from a frontal view.  
In reality, the images of a subject to be identified are rarely taken from a frontal or profile view. Surveillance cameras mainly capture people from above and this factor can be decisive in investigations. Therefore, having a 3D model of a suspect's face would allow forensic experts in facial recognition to improve the alignment of the known face with anonymous images captured by CCTV. The study represents an initial feasibility analysis for the design of a multi-vision system for facial reconstruction. Further development, such as acquiring a complete synchronized multi-view system and creating a dedicated environment for system development can only enhance these preliminary results.  
Conceptualization, P.L., C.C. and C.N.; data curation, S.G. and F.T.; formal analysis, C.N.; methodology, P.L., C.C. and C.N.; software, S.G. and F.T.; validation, S.G. and F.T.; writing--original draft, C.N. All authors have read and agreed to the published version of the manuscript. This research received no funding.  
Ethical review and approval were waived for this study due it was required only informed consent.  
Written informed consent has been obtained from all subjects involved in the study to publish this paper.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.