A BAYESIAN HIERARCHICAL MODEL FOR EVALUATING FORENSIC FOOTWEAR EVIDENCE

• 成果类型: Article
• 署名作者: Spencer, Neil A.; Murray, Jared S.
• 署名单位: Carnegie Mellon University; Carnegie Mellon University; University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/20-AOAS1334
• 发表日期: 2020
• 页码: 1449-1470
• 关键词: randomly acquired characteristics; IMPRESSIONS; patterns; shape

摘要

When a latent shoeprint is discovered at a crime scene, forensic analysts inspect it for distinctive patterns of wear such as scratches and holes (known as accidentals) on the source shoe's sole. If its accidentals correspond to those of a suspect's shoe, the print can be used as forensic evidence to place the suspect at the crime scene. The strength of this evidence depends on the random match probability-the chance that a shoe chosen at random would match the crime scene print's accidentals. Evaluating random match probabilities requires an accurate model for the spatial distribution of accidentals on shoe soles. A recent report by the President's Council of Advisors in Science and Technology criticized existing models in the literature, calling for new empirically validated techniques. We respond to this request with a new spatial point process model (code and synthetic data is available as Supplementary Material) for accidental locations, developed within a hierarchical Bayesian framework. We treat the tread pattern of each shoe as a covariate, allowing us to pool information across large heterogeneous databases of shoes. Existing models ignore this information; our results show that including it leads to significantly better model fit. We demonstrate this by fitting our model to one such database.