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Statistical Discrimination of Footwear: A Method for the Comparison of Accidentals on Shoe Outsoles Inspired by Facial Recognition Techniques
Author(s) -
Petraco Nicholas D. K.,
Gambino Carol,
Kubic Thomas A.,
Olivio Dayhana,
Petraco Nicholas
Publication year - 2010
Publication title -
journal of forensic sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.715
H-Index - 96
eISSN - 1556-4029
pISSN - 0022-1198
DOI - 10.1111/j.1556-4029.2009.01209.x
Subject(s) - accidental , computer science , set (abstract data type) , identification (biology) , similarity (geometry) , artificial intelligence , face (sociological concept) , pattern recognition (psychology) , mathematics , statistics , social science , botany , physics , sociology , acoustics , image (mathematics) , biology , programming language
In the field of forensic footwear examination, it is a widely held belief that patterns of accidental marks found on footwear and footwear impressions possess a high degree of “uniqueness.” This belief, however, has not been thoroughly studied in a numerical way using controlled experiments. As a result, this form of valuable physical evidence has been the subject of admissibility challenges. In this study, we apply statistical techniques used in facial pattern recognition, to a minimal set of information gleaned from accidental patterns. That is, in order to maximize the amount of potential similarity between patterns, we only use the coordinate locations of accidental marks (on the top portion of a footwear impression) to characterize the entire pattern. This allows us to numerically gauge how similar two patterns are to one another in a worst‐case scenario, i.e., in the absence of a tremendous amount of information normally available to the footwear examiner such as accidental mark size and shape. The patterns were recorded from the top portion of the shoe soles (i.e., not the heel) of five shoe pairs. All shoes were the same make and model and all were worn by the same person for a period of 30 days. We found that in 20–30 dimensional principal component (PC) space (99.5% variance retained), patterns from the same shoe, even at different points in time, tended to cluster closer to each other than patterns from different shoes. Correct shoe identification rates using maximum likelihood linear classification analysis and the hold‐one‐out procedure ranged from 81% to 100%. Although low in variance, three‐dimensional PC plots were made and generally corroborated the findings in the much higher dimensional PC‐space. This study is intended to be a starting point for future research to build statistical models on the formation and evolution of accidental patterns.