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ShoeCase: A data set of mock crime scene footwear impressions
Abigail Tibben1, Megan McGuire1, Stacy Renfro1
1Iowa State University: 3410 Beardshear Hall, Ames, IA 50011 CSAFE: 195 Durham Center 613 Morrill Road Ames, IA 50011, USA.
Abstract:
This project's main objective is to create an open-source database containing a sizeable number of high-quality images of shoe impressions. The Center for Statistics and Applications in Forensic Evidence (CSAFE) team collected images that represented those found at crime scenes and constructed a database that is publicly available to forensic science and research communities. The database includes images obtained from mixed impression types: full blood impression, partial blood impression, and dust impression. The impressions are made on different flooring (vinyl and tile), and captured via various lift techniques: gel lifts and handiprints (exemplar prints with high definition using graphite powder and clear sticky vinyl, backed by a white sheet), and saved in multiple digital file types (TIF, XMP, CR3, and JPG). Our data were collected to ensure reproducibility, using simple but well-described protocols and easily accessible materials. The complete dataset includes 936 unique shoeprint images saved in 3,275 digital files. Data were collected by trained volunteers making shoe impressions on flooring with the two mediums: spatter blood and graphite powder. To make an impression, volunteers wearing a shoe stepped into the material and then walked on the flooring. A separate "lighter" step was taken to create a partial print for the blood prints. The blood prints were brought to the photography station, where researchers labeled and photographed them. Graphite prints were covered with a gel lifter before being moved to the photography room. There, the researchers removed the lift, labeled, and then photographed them. Our data will be of significant use to researchers, examiners, and anyone who could benefit from using a large dataset like this. Footwear datasets are often difficult to find, especially ones that resemble crime scenes, so our data can help fill that gap.
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