## 1 Introduction
Figure 2[3] shows examples of alterations to the position among facial components and the effect those position have on the overall face composition. It is important to emphasize that the respect of proportions required by the FICWG guidelines is met if the reconstruction minimizes differences from the real face. Figure 1: Variation in the appearance of the same individual at different camera angles (from [2]). According to this requirement, it can be assumed that a 3D face reconstruction (3 DFR) that is closer to the subject will produce a 3D mug shot that is close to the real one. It is difficult to define the required accuracy, relative or absolute, because, to the best of our knowledge, we have not encountered any explicit references to establish an error threshold, but at this preliminary stage, a threshold of no more than 1 mm error seems reasonable. Techniques using laser or structured light have achieved sub-millimetre accuracy in the production of 3D models. However, the long acquisition time makes them less suitable for producing a 3D face model in a non-cooperative scenario, such as that of an arrested person. In [6], [PERSON] et al., 2024 compare a stereo photogrammetric system (3 dDM) with two handheld structured light scanners, the Artec Eva and the ArtecEva Spider from Artec 3D. The acquisition time is 20 s for the first system and 60 s for the other one against 0.15 ms for the photogrammetric system. A short exposure time is achieved through camera settings like shutter speed. Typically, fast shutter speeds, such as 1/250 th of a second (4 ms) or faster, will freeze and capture quick-moving action, resulting in a clear image of a subject that would otherwise be blurred. In recent years, 3D facial reconstruction technology based on imagery has made rapid development. Systems for 3D face reconstruction (3 DFR) based on imagery are currently being developed by several researchers [7; 8; 9]. More recent approaches are those presented in [10] where [PERSON] et al., 2024 summarize the most relevant algorithms for 3D facial reconstruction based on a single image or two images as the frontal and lateral images of a conventional mug shot. More specifically, methods based on a 3D Morphable Model and deep learning have attracted research interest. [PERSON] et al., 2024 also describe the available public dataset that allows realization of a quantitative analysis with respect to a ground truth and a comparison between methods. Reconstruction algorithms may be based on mug shots or sequences of video frames, and their aim is to improve the matching against an existing mug shot database. In [11] [PERSON] et al., 2022 provide a comprehensive and up-to-date review of the state of the art in 3 DFR algorithms for forensic application. Algorithms suitable for forensic applications should satisfy constraints leading to the legal validity of the conclusion during a lawsuit or in the investigation phase. The 3 DFR approaches were divided into two main groups based on whether they are evidence  
Figure 2: Examples of alteration to the positions among facial components and the effect those positions have on the overall face/head composition (from [3]). based, such as CCTV frames or based on a 2D mug shot or photographs--the so called model-approach. The approaches were evaluated against the essential requirements of a forensic system, including robustness to facial ageing and pose variation, robustness to occlusions, use of facial scars and marks, and adherence to biometrics characteristics. The model-based approach allows the strategy of introducing a gallery of various predefined poses in the 2D domain to enhance the representation capability. The author analyses the 3 DFR obtained from the 2D mug shots (frontal and lateral view) or from one or multiple images.