![]() While this model is not ready to roll out to crime labs around the country, Spencer believes it is important to have this community weighing the limitations of current methods when developing conclusions. In this project, the authors sought to address a need to validate forensic techniques after a call by the National Research Council in 2009, which was echoed in 2016 by the President's Council of Advisors on Science and Technology. Their models more successfully fit the location of accidentals on shoes than current models. The researchers pooled the information across many different types of shoes in order to capture similarities in how contact surfaces affect the distribution of accidentals on the sole. The team based their model on one of the largest in shoeprint databases in the world, an Israeli police database consisting of 400 samples. Spencer and Murray developed several models that fold in multiple layers of shoe complexity, from shoe shape to contact surface, to understand the randomness of accidental occurrence. "Small changes in modeling assumptions can shift these measures by orders of magnitude, so it is vital to get them right."Ĭurrent models use a hypothetical shoe without considering other contributing factors, like shoe shape and the presence of arches. "Any quantitative measure of the strength of evidence of a shoeprint match will be highly sensitive to underlying probability models," said Murray, senior author on the paper. Related Article: World's First Lab to Study Human Decomposition in Cold Climate The strength of shoe print evidence is based on probability-what are the chances that a random shoe would produce the same pattern of accidentals? This sounds reasonable, but the reliability of the match hinges on an accurate model for the spatial distribution of accidentals on the soles of shoes. Experts pair shoe print evidence with a random match probability to communicate the level of uncertainty associated with the results For this reason, there will always be some uncertainty concerning whether a suspect's shoe truly matches the crime scene print, or if the match is simply a false positive. ![]() This technique is further complicated by print variability during the impression-taking process. Unfortunately, latent crime scene prints are often low quality, reducing accurate identification of accidentals. ![]() Spencer and his co-author Jared Murray, assistant professor of statistics at the University of Texas at Austin, published their study online in the journal of Annals of Applied Statistics.Įxaminers compare shoe print evidence collected at a crime scene to the suspect's shoes using class characteristics (shoe brand, model, and size) and accidentals-the unique patterns produced during wear. "We decided to examine the assumptions in this data-scarce field to help us identify gaps in the existing models that could lead to incorrect or overconfident conclusions." "People tend to look at like fingerprints, but existing models in the literature are simple and produced pretty strong conclusions without much investigation into the assumptions behind the model," said Spencer. Neil Spencer, a PhD, a student in the joint Statistics and Machine Learning doctoral program at Carnegie Mellon University, sought to add clarity to one commonly used forensic technique, shoe print identification. ![]() By subscribing, you agree to receive email related to Lab Manager content and products.
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