Optimization of a Statistical Algorithm for Objective Comparison of Toolmarks

• Ryan Spotts ^[1] ; L. Scott Chumbley ^[1] ; Laura Ekstrand ^[1] ; Song Zhang ^[1] ; James Kreiser ^[2]
  1. [1] Iowa State University

     Iowa State University

     Township of Franklin, Estados Unidos

     
  2. [2] Illinois State Police Springfield IL
• Localización: Journal of forensic sciences, ISSN-e 1556-4029, ISSN 0022-1198, Vol. 60, Nº. 2, 2015, págs. 303-314
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • Due to historical legal challenges, there is a driving force for the development of objective methods of forensic toolmark identification. This study utilizes an algorithm to separate matching and nonmatching shear cut toolmarks created using fifty sequentially manufac- tured pliers. Unlike previously analyzed striated screwdriver marks, shear cut marks contain discontinuous groups of striations, posing a more difficult test of algorithm applicability. The algorithm compares correlation between optical 3D toolmark topography data, producing a Wilcoxon rank sum test statistic. Relative magnitude of this metric separates the matching and nonmatching toolmarks. Results show a high degree of statistical separation between matching and nonmatching distributions. Further separation is achieved with optimized input parameters and implementation of a “leash” preventing a previous source of outliers —however complete statistical separation was not achieved. This paper represents further development of objective methods of toolmark identification and further validation of the assumption that toolmarks are identifiably unique.