Empirical Evaluation of the Reliability of Photogrammetry Software in the Recovery of Three-Dimensional Footwear Impressions.

This paper examines the reliability of Structure from Motion (SfM) photogrammetry as a tool in the capture of forensic footwear marks. This is applicable to photogrammetry freeware DigTrace but is equally relevant to other SfM solutions. SfM simply requires a digital camera, a scale bar, and a selection of oblique photographs of the trace in question taken at the scene. The output is a digital three-dimensional point cloud of the surface and any plastic trace thereon. The first section of this paper examines the reliability of photogrammetry to capture the same data when repeatedly used on one impression, while the second part assesses the impact of varying cameras. Using cloud to cloud comparisons that measure the distance between two-point clouds, we assess the variability between models. The results highlight how little variability is evident and therefore speak to the accuracy and consistency of such techniques in the capture of three-dimensional traces. Using this method, 3D footwear impressions can, in many substrates, be collected with a repeatability of 97% with any variation between models less than ~0.5 mm

An algorithm to compare two‐dimensional footwear outsole images using maximum cliques and speeded‐up robust feature

Footwear examiners are tasked with comparing an outsole impression (Q) left at a crime scene with an impression (K) from a database or from the suspect\u27s shoe. We propose a method for comparing two shoe outsole impressions that relies on robust features (speeded‐up robust feature; SURF) on each impression and aligns them using a maximum clique (MC). After alignment, an algorithm we denote MC‐COMP is used to extract additional features that are then combined into a univariate similarity score using a random forest (RF). We use a database of shoe outsole impressions that includes images from two models of athletic shoes that were purchased new and then worn by study participants for about 6 months. The shoes share class characteristics such as outsole pattern and size, and thus the comparison is challenging. We find that the RF implemented on SURF outperforms other methods recently proposed in the literature in terms of classification precision. In more realistic scenarios where crime scene impressions may be degraded and smudged, the algorithm we propose—denoted MC‐COMP‐SURF—shows the best classification performance by detecting unique features better than other methods. The algorithm can be implemented with the R‐package shoeprintr

A Preliminary Investigation into the Accuracy of 3D Modeling and 3D Printing in Forensic Anthropology Evidence Reconstruction

There is currently no published empirical evidence-base demonstrating 3D printing to be an accurate and reliable tool in forensic anthropology, despite 3D printed replicas being exhibited as demonstrative evidence in court. In this study, human bones (n = 3) scanned using computed tomography were reconstructed as virtual 3D models (n = 6), and 3D printed using six commercially available printers, with osteometric data recorded at each stage. Virtual models and 3D prints were on average accurate to the source bones, with mean differences from -0.4 to 1.2 mm (-0.4% to 12.0%). Interobserver differences ranged from -5.1 to 0.7 mm (-5.3% to 0.7%). Reconstruction and modeling parameters influenced accuracy, and prints produced using selective laser sintering (SLS) were most consistently accurate. This preliminary investigation into virtual modeling and 3D printer capability provides a novel insight into the accuracy of 3D printing osteological samples and begins to establish an evidence-base for validating 3D printed bones as demonstrative evidence