Shoeprints are a common type of evidence found at crime scenes and are regularly used in forensic investigations. However, their utility is limited by the lack of reference footwear databases
When a crime is committed, law enforcement directs crime scene experts to obtain evidence that may be pertinent to identifying the perpetrator(s). Much of this evidence comes in the form
Shoeprints are aligned before assessing similarity, and automatic alignment algorithms can handle differences in translation, rotation [1], and scale. But shoeprints recorded at a crime scene may be partials photographed
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)
In forensic practice, evaluating shoeprint evidence is challenging because the differences between images of two different outsoles can be subtle. In this paper, we propose a deep transfer learning-based matching
We introduce an algorithmic approach designed to compare similar shoeprint images, with automated alignment. Our method employs the Iterative Closest Points (ICP) algorithm to attain optimal alignment, further enhancing precision
We introduce a semi-automatic alignment tool tailored for two similar footwear impressions. The term “semi-automatic” is used because the alignment process is primarily automated, yet users have the flexibility to
This presentation is from the 107th International Association for Identification (IAI) Annual Educational Conference, National Harbor, Maryland, August 20-26, 2023. Posted with permission of CSAFE.
This presentation is from the 107th International Association for Identification (IAI) Annual Educational Conference, National Harbor, Maryland, August 20-26, 2023. Posted with permission of CSAFE.