## 1 Introduction
The conclusion of [PERSON] et al., 2022 is that a rigorous photogrammetric approach based on a large number of images is the recommended solution to ensure that the criteria are met in a way that is both effective and reliable, specifically in terms of high quality of biometric characteristics. Photogrammetry uses two or more cameras to extract 3D information about the target object, such as a face [12]. The output is a point cloud that can be used to generate a 3D model of the object. Texture information (RGB values) can be associated with each point to produce a photorealistic model. The use of a large number of synchronized cameras is essential to capture every part of the face redundantly due to the complexity of facial geometry. A large number of overlapping images is mandatory not only to guarantee the coverage of the whole face but also to optimize the image orientation and image alignment. Photogrammetric systems for facial or human body reconstruction are effectively applied in applications in the entertainment industry and in the medical field. The commercial company, Xangle Studio [13], provides 3D human body reconstruction for film visual effects and games. It uses about 100 full-frame cameras for head reconstruction and about 200 full-frame cameras for body reconstruction. 3D stereo photogrammetric imaging systems are increasingly used in clinical and research settings for facial surgery and rhinoplasty [14]. The 3 dMD System [15] is one of the most widely used imaging systems currently on the market [16; 17]. In this system, a random light pattern is projected onto the subject while precisely synchronized cameras capture images from different angles according to an optimal configuration. The accuracy reported by the manufacturer is 0.2 mm. In [6], [PERSON] et al., 2024 verify the system reliability of the 3 dDM system by scanning the head of a mannequin and the faces of healthy volunteers several times, achieving a mean error of 0.23 mm on a few reference distances. The Botscan system by [PERSON] [18] uses 70 synchronized DSLR cameras to simultaneously capture images of a standing person for 3D reconstructions. In [19] ([PERSON] et al., 2018) compare measurements acquired on a 3D model realized with the Botscan system and Agisoft software against the measurements extracted from forensic photographs. The measurements obtained by photogrammetry were significantly more accurate than those obtained by standard forensic methods based on 2D mug shots, with mean differences of 1.5 mm compared to 3.6 mm. In [20], [PERSON] et al., 2019 present a 3D mug shot system specifically developed for forensic identification using a photogrammetric approach. The system comprises 26 digital single-lens reflex (DLSR) CANON EOS 80D cameras arranged in a semicircle with a radius of 1.46 m. The model is scaled with a single reference distance, and its validation is primarily focused on capturing the morphological facial features analyzing different focal distances. Full-frame cameras, known for their high performance and high-resolution characteristics, ensure accuracy, minimal noise, and distortion, especially when paired with top-quality lenses. The primary goal of this research is to evaluate the potential of using low-cost cameras to achieve submillimetre accuracy in reconstructing 3D facial models. In collaboration with the Scientific Investigations Department (RIS) in Rome, we conducted a pilot study, developing a multi-view system utilizing high-resolution Raspberry sensors [21]. These sensors offer a significant cost advantage compared to full-frame cameras. The system's performance was assessed by acquiring a 3D model and analyzing the point-wise reconstruction error.