## 3 Results
### Test in a Real Setting
#### 3.2.2 Three-Dimensional Mug Shot vs. 2D Mug Shot  
We tested our system with seven volunteers. We are aware of the limitations of this kind of test due to the lack of synchronization of all the frames among all the frames, since we took all the image in sequence with the relocation of the \(3\) cameras to cover all the angles. Images were taken in groups of \(3\), so that any movement of the subject would prevent  
Figure 14: Reconstruction errors. Errors up to \(\pm 1\) mm are mapped on the model. The green colour corresponds to a few tenths of a millimetre. Figure 15: Error distribution in the range from \(-5\) mm to \(5\) mm for the model scaled with a single distance (blue line) and with \(10\) distances (red line). the face from becoming reconstructed. Despite that, three out of seven subjects were able to maintain their position for the entire acquisition step that lasted several minutes. This allowed us to obtain images that were suitable for producing a quasi-complete 3D face model. Results could only be analyzed through visual analysis. Figure 16 shows half of the original frontal images with the half-face of the 3D model. For one volunteer, the final subject in Figure 16, who positioned his head against the wall for stability, we were able to reconstruct a complete 3D model. This allowed us to evaluate the performance of facial recognition using the 3D mugshot compared to the conventional 2D mugshot within NeoFace Watching [26] software routinely used by RIS investigators. This software performs a one-to-one comparison between a trace image and a database of 2D mug shot, producing, as a result, a score from 0 to 1. A value close to 0 represents 'no match', while close to 1 confirms a good matching. A score exactly equal to 1 indicates the same person. This is one of the techniques used by the investigators to confirm the identity of a suspect. The subject depicted in Figure 17 shows the images of the volunteer used in this test. Figure 17a shows the original frontal image, Figure 17d shows the lateral image. Figure 17b shows the textured point cloud produced by Agisoft Metashape (black areas represent unreconstructed points), and Figure 17c shows a lateral view of the 3D model. The texture covers small data gaps but cannot cover a large area without introducing deformation. By comparing the image with the model, we can observe that small details, such as nevi and wrinkles expression, are represented. From the 3D mug shot, we extracted fifteen poses in different orientations, as illustrated in Figure 18, and we included them in the mug shot database. Figure 16: Comparison of imagery of half face (on the left) and 3D model of half face (on the right). Figure 17: (**a**) Original frontal image; (**b**) point cloud, frontal view; (**c**) textured model, lateral view; (**d**) original lateral image. Initially, we sought to find similarities between the frontal image of Figure 17a (used as trace image) and the fifteen frames extracted from the 3D model, as well as the frontal image itself. As expected, we obtained a matching score equal to 1 when comparing the frontal image (Figure 19(1)) with itself and a score higher than 0.97 for four frames extracted from the 3D model (Figure 19(2-5)), confirming the reliability of the produced model (Figure 19). The subfigure 1-5 are the output of the NeoFace Watch software. To further examine the performance of face matching of a 3D mug shot against the 2D mug shot we extracted 12 frames from videos acquired by two surveillance cameras (Figure 20). The subject is shown in different poses and at varying scales due to the distances from the CCTV cameras. The two video surveillance cameras have a resolution of 1920 \(\times\) 1080. To select the area of the face, the first eight frames were cropped at a resolution of 686 \(\times\) 571 pixels, (Figure 20(1-8)) while the last four at a resolution of 592 \(\times\) 601 pixel (Figure 20(9-12)).