**4.2. Gaming sensors and Forensic analysis**

Gaming sensors were designed as motion sensors for the entertainment industry. Opposite to the active laser system, they do not generate directly a 3D point cloud. Instead, they create a range image, where every pixel of 2D image is linked to the distance or depth. Their potential for the 3D mapping of objects and indoor environments was recently discovered, opening new fields of applications in forensics (**Figure 10**). These sensors, also known as RGB‐D cameras, are designed based on high frame‐rate data acquisition. The main disadvantages are their low accuracy in distance measurement, and their low spatial resolution (understood as pixel size). Despite these drawbacks, these new RGB‐D sensors are a low‐cost alternative to other well‐ established active laser systems, such as the TLS or photonic mixer devices (PMD). Their autonomy, portability and high acquisition frame‐rate have revolutionized the field of 3D documentation [22].

The first generation of gaming sensors had a quick diffusion, both in the entertainment industry and in scientific community [23], being especially analysed from a geometrical point of view, since different sources of noise could affect them. However, the second generation of gaming sensors, distributed in mid‐2014, has been the subject of a limited number of study

**Figure 10.** Gaming sensor employed for crime scene documentation. Image courtesy of Faro.

ourable conditions; (2) the guarantying of quality in results by means of robust procedures providing greater accuracy and reliability; (3) flexibility, by making it possible to work with any type of image (visible, thermal, infrared, etc.) and any type of camera (calibrated and non‐ calibrated). However, the main milestones regarding image‐based modelling currently are twofold: (i) cope with texture‐less objects (ii) obtaining CAD models from point clouds, which

Since texture‐less objects could appear in any kind of forensic scenario, in the first stage of 3D reconstruction (interest point detection), the matching results will be poor. That is because the local image features (through their descriptors) are unable to provide a robust correspondence among them. For this reason, they present a significant challenge in computer vision and photogrammetry, especially for the image detectors and descriptors. If high amounts of texture‐less object are present in the scene, the number of wrong correspondences in the image orientation process (see Section 3.1) could surpass the efficiency of robust methods, and as a

The most novel automatic approaches to afford nonparametric forms are based on 2D and 3D triangulation strategies, which generate a surface model. Nonetheless, this falls far from what is expected of CAD models in forensic analysis, which must be shaped as solid models with topological relations and properties. Nowadays, there are only semi‐automatic approaches to generate solid models based on three methodologies: (1) fitting of basic primitives for those simple objects which are represented by means of a parametric shape; (2) performing cross‐ sections that, accompanied by shape extrusion operations, enable the generation of the corresponding solid model; (3) fitting of more complex functions of B‐spline type (NURBS‐ non‐uniform rational B‐spline) which, through cross‐sections and sweeping operations all along a path make it possible to generate the solid model. However, all of these methods need manual interaction at the moment of point clouds segmentation and results refinement.

Gaming sensors were designed as motion sensors for the entertainment industry. Opposite to the active laser system, they do not generate directly a 3D point cloud. Instead, they create a range image, where every pixel of 2D image is linked to the distance or depth. Their potential for the 3D mapping of objects and indoor environments was recently discovered, opening new fields of applications in forensics (**Figure 10**). These sensors, also known as RGB‐D cameras, are designed based on high frame‐rate data acquisition. The main disadvantages are their low accuracy in distance measurement, and their low spatial resolution (understood as pixel size). Despite these drawbacks, these new RGB‐D sensors are a low‐cost alternative to other well‐ established active laser systems, such as the TLS or photonic mixer devices (PMD). Their autonomy, portability and high acquisition frame‐rate have revolutionized the field of 3D

The first generation of gaming sensors had a quick diffusion, both in the entertainment industry and in scientific community [23], being especially analysed from a geometrical point of view, since different sources of noise could affect them. However, the second generation of gaming sensors, distributed in mid‐2014, has been the subject of a limited number of study

result, the camera orientation would be incorrectly determined.

are more useful in forensic analysis.

18 Forensic Analysis - From Death to Justice

**4.2. Gaming sensors and Forensic analysis**

documentation [22].

cases. In addition, they were released with severe changes in the measurement system, which is different from the former generation, based on the structured light principle. The new ones incorporate time‐of‐flight technology, increasing the spatial resolution and allowing the possibility of working outdoors.

Due to its recentness, their applicability in forensic science is being proved by recent studies such as crime scene modelling [24]; post‐mortem analysis [25]; body measurements for anthropometric purposes [26]; and gait recognition [27], becoming a useful tool since does not require the collaboration of the subject. Alternative applications of gaming sensors are real‐ time surveillance and biometric studies, as face recognition and face analysis [28]. At these tasks, the active light source could cope with the illumination changes of RGB passive methods (which could disturb the final 3D model), making gaming sensor an inexpensive way for real‐ time analysis. However, the resulting range image could be affected by some geometrical problems such as the presence of holes in the image due to occlusions, the inaccurate depth computations, the measurement noise and the low spatial resolution.

Nowadays, these problems are overcoming by the second generation of gaming sensors and the new developments of kinect fusion libraries [29]. These libraries are focused in solving the position of the individual range images and merge them into a 3D scene by means of a volumetric integration.

Together with the advances in gaming sensors, some promising outcomes are being developed in forensic face analysis thorough local feature extraction methods [30]. Feature extraction can be driven in two ways: by means of the position and shape of facial features, known as geometric‐based; or by means of a construction of global/local descriptor also known as appearance based. The last ones are widely used, being the local face descriptors those which have better performance in non‐controlled environments.

In biometric forensic studies, a common disadvantage of gaming sensors and visible methods is their vulnerability to spoofing (synthetic forged version of the biometric original). This weakness is being overcome by thermographic cameras. The new challenges try to solve the integration of different electromagnetic range images, for example thermal with visible, dealing with a problem known as multimodal matching [31].
