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Empirically Observed and Predicted Estimates of Chance association: Estimating the Chance Association of Randomly Acquired Characteristics in Footwear Comparisons

NCJ Number
253972
Date Published
Author(s)
Nicole Richetelli, William J. Bodziak, Jacqueline A. Speir
Annotation
Since the degree to which a feature might repeat by chance alone is less well documented in many pattern science fields, including forensic footwear analysis, this study assessed this chance by examining the mathematical similarity of just over 3.2 million pairwise RAC comparisons as a function of more than 72,000 RACs collected from 1,300 unrelated outsoles.
Abstract
The power associated with demonstrating a linkage between footwear and an impression left at the scene of a crime is directly related to the perceived rarity of the shoeprint itself. When individualizing characteristics are present, their relative position, orientation, size and shape are examined and compared with known exemplars to establish the strength of the suspected linkage. In the current study, the resulting similarity scores were sorted, and more than 91,000 of the mathematically most similar known non-match RACs with positional co-occurrence were visually assessed (in duplicate by two analysts) to determine their degree of observable resemblance. These empirical estimates of visual similarity were used to model resemblance as a Bernoulli distribution with binary outcomes indistinguishable/distinguishable). Using a logistic regression, the conditional probability of sufficient resemblance, given a mathematical match score, or p(indistinguishability score), was estimated and used to predict the likelihood of encountering indistinguishable features for the remaining less-similar 1.0 million RAC pairs in the dataset with the same geometric/shape categorization (linear, compact or variable). Part 1 of this effort reports the intersection of co-occurrence in spatial position and resemblance with results indicating that the median estimate of indistinguishability based on the upper 95 percent credible interval for estimation (or worst-case scenario) is 1 in 444,126 for linear, 1 in 291,111 for compact, and 1 in 880,774 for variable features. Part 2 of this effort will report random match probabilities for the same dataset. (Publisher abstract modified)
Date Created: December 1, 2019