## 2 Materials and Methods  
Before delving into the methodology, it is crucial to understand the entire photogrammetric process involved in producing a 3D mug shot. An object, defined within an established 3D world frame, is captured using two or more images. Each image is acquired from a unique camera position, defining a 3D camera frame with its origin at the camera's projection centre and its z-axis aligned with the optical axis of the camera. For each camera frame, the three coordinates of the projection centre in the 3D world frame and the three rotations needed to align the 3D camera frame with the 3D world frame define the orientation parameter of the six cameras. The external parameters establish the relationships between the 3D world frame and the individual camera frames. The photogrammetric process can be divided in three stages. The first stage is the acquisition step (Figure 3a), where each image is captured from a different camera location according to a predetermined network design that defines the orientation parameters, \(T\), of each camera. To ensure accurate facial reconstruction, the images should provide full coverage from ear to ear, with at least a double coverage for every facial feature. Additionally, cameras must be synchronized to capture all images simultaneously in a single shot minimizing motion blur caused by subject movements. The second stage (Figure 3b) involves the orientation process, where the orientation parameters, \(T\), are estimated using a set of points that allow the writing of projective relationships between the images. Key points for image alignment are primarily tie points (TPs), which connect images sharing common object features. Automatic methods for detecting corresponding interest points between images often generate a very large number of TPs. Ground control points (GCPs), in addition to tie points (TPs), are a limited set of points with precisely known coordinates in an external reference frame. For high-accuracy surveys, these points are often marked and can be located in images with sub-pixel precision. The knowledge of TPs is crucial for the second stage of the process and is derived entirely from the images. This step can be approached in two different ways, depending on whether we are working solely with TPs or with TPs and GCPs. In the first case, the object will be reconstructed in a relative frame, whereby the orientation parameters of all cameras are determined relative to each other (relative orientation). The resulting 3D model is out of scale and not oriented. In the second case, the knowledge of the GCPs provides a 3D model that is both scaled and oriented (external orientation). The third stage (Figure 4) involves object reconstruction based on the acquired images and known orientation parameters. This process relies on a dense matching algorithm and generates a point cloud representing the object either in a relative frame or in the world frame. Figure 4: The photogrammetric stage: reconstruction stage. Figure 3: The photogrammetric stage: (**a**) acquisition stage; (**b**) orientation stage. As in any photogrammetric survey for automated surface reconstruction, image orientation is a fundamental aspect of the process. To improve the robustness of the multi-view system in determining orientation parameters, a higher coverage than double is recommended. Additionally, accurate face reconstruction must account for intrinsic facial movements. This necessitates very short image acquisition times, achievable only with multiple synchronized cameras. Our study presents some preliminary results of a feasibility study for a low-cost photogrammetric system for 3D face reconstruction in forensic, aiming for submillimetre accuracy. The low-cost camera we tested is a Raspberry, which is particularly interesting because it supports C-mount lenses. The main challenge with low-cost cameras is their lower signal-to-noise ratio due to smaller sensors. Additionally, smaller sensors limit the field of view, which is another obstacle to overcome.