title: Addressing the National Academy of Sciences' challenge: a method for statistical pattern comparison of striated tool marks.
abstract: In February 2009, the National Academy of Sciences published a report entitled "Strengthening Forensic Science in the United States: A Path Forward." The report notes research studies must be performed to "…understand the reliability and repeatability…" of comparison methods commonly used in forensic science. Numerical classification methods have the ability to assign objective quantitative measures to these words. In this study, reproducible sets of ideal striation patterns were made with nine slotted screwdrivers, encoded into high-dimensional feature vectors, and subjected to multiple statistical pattern recognition methods. The specific methods employed were chosen because of their long peer-reviewed track records, widespread successful use for both industry and academic applications, rely on few assumptions on the data's underlying distribution, can be accompanied by standard confidence levels, and are falsifiable. For PLS-DA, correct classification rates of 97% or higher were achieved by retaining only eight dimensions (8D) of data. PCA-SVM required even fewer dimensions, 4D, for the same level of performance. Finally, for the first time in forensic science, it is shown how to use conformal prediction theory to compute identifications of striation patterns at a given level of confidence.
authors.affiliation: Department of Sciences, John Jay College of Criminal Justice, City University of New York, New York, NY 10019, USA. npetraco@jjay.cuny.edu
authors.forename: Nicholas D K
authors.initials: ND
authors.lastname: Petraco
authors.forename: Peter
authors.initials: P
authors.lastname: Shenkin
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Speir
authors.forename: Peter
authors.initials: P
authors.lastname: Diaczuk
authors.forename: Peter A
authors.initials: PA
authors.lastname: Pizzola
authors.forename: Carol
authors.initials: C
authors.lastname: Gambino
authors.forename: Nicholas
authors.initials: N
authors.lastname: Petraco
mesh_terms: D016002:Discriminant Analysis; D004867:Equipment Design; D044707:Forensic Sciences / Q000379:methods; D006801:Humans; D015999:Multivariate Analysis; D025341:Principal Component Analysis; D060388:Support Vector Machine; D054041:Weapons; D014947:Wounds and Injuries / Q000473:pathology