Addressing the National Academy of Sciences’ Challenge: A Method for Statistical Pattern Comparison of Striated Tool Marks
Article first published online: 3 APR 2012
© 2012 American Academy of Forensic Sciences
Journal of Forensic Sciences
Volume 57, Issue 4, pages 900–911, July 2012
How to Cite
Petraco, N. D. K., Shenkin, P., Speir, J., Diaczuk, P., Pizzola, P. A., Gambino, C. and Petraco, N. (2012), Addressing the National Academy of Sciences’ Challenge: A Method for Statistical Pattern Comparison of Striated Tool Marks. Journal of Forensic Sciences, 57: 900–911. doi: 10.1111/j.1556-4029.2012.02115.x
- Issue published online: 2 JUL 2012
- Article first published online: 3 APR 2012
- Received 25 Dec. 2009; and in revised form 25 April 2011; accepted 4 June 2011.
- forensic science;
- National Academy of Sciences;
- tool marks;
- pattern recognition;
- error rates
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.