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Statistical Discrimination of Footwear: A Method for the Comparison of Accidentals on Shoe Outsoles Inspired by Facial Recognition Techniques

Authors

  • Nicholas D. K. Petraco Ph.D.,

    1. Department of Science, John Jay College of Criminal Justice, City University of New York, 899 10th Avenue, New York, NY 10019.
    2. Faculty of Chemistry, Graduate Center, City University of New York, 365 5th Avenue, New York, NY 10016.
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  • Carol Gambino M.S.,

    1. Department of Science, John Jay College of Criminal Justice, City University of New York, 899 10th Avenue, New York, NY 10019.
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  • Thomas A. Kubic Ph.D.,

    1. Department of Science, John Jay College of Criminal Justice, City University of New York, 899 10th Avenue, New York, NY 10019.
    2. Faculty of Chemistry, Graduate Center, City University of New York, 365 5th Avenue, New York, NY 10016.
    3. Faculty of Forensic Science, Graduate Center, City University of New York, 365 5th Avenue, New York, NY 10016.
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  • Dayhana Olivio,

    1. Department of Science, John Jay College of Criminal Justice, City University of New York, 899 10th Avenue, New York, NY 10019.
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  • Nicholas Petraco M.S.

    1. Department of Science, John Jay College of Criminal Justice, City University of New York, 899 10th Avenue, New York, NY 10019.
    2. Faculty of Forensic Science, Graduate Center, City University of New York, 365 5th Avenue, New York, NY 10016.
    3. New York City Police Department Crime Laboratory, Trace Evidence Unit, 150-14 Jamaica Avenue, Jamaica, NY 11432.
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Additional information and reprint requests:
Nicholas D. K. Petraco, Ph.D.
Department of Science
John Jay College of Criminal Justice
899 10th Avenue
New York
NY 10019
E-mail: npetraco@jjay.cuny.edu

Abstract

Abstract:  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.

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