Application of Machine Learning to Toolmarks: Statistically Based Methods for Impression Pattern Comparisons

Nicholas D. K. Petraco, Ph.D.; Helen Chan, B.A.; Peter R. De Forest, D.Crim.; Peter Diaczuk, M.S.; Carol Gambino, M.S., James Hamby, Ph.D.; Frani L. Kammerman, M.S.; Brooke W. Kammrath, M.A., M.S; Thomas A. Kubic, M.S., J.D., Ph.D.; Loretta Kuo, M.S.; Patrick McLaughlin; Gerard Petillo, B.A.; Nicholas Petraco, M.S.; Elizabeth W. Phelps, M.S.; Peter A. Pizzola, Ph.D.; Dale K. Purcell, M.S.; Peter Shenkin, Ph.D.

Abstract

Over the last decade, forensic firearms and toolmark examiners have encountered harsh criticism that there is no accepted methodology to generate numerical “proof” that independently corroborates their morphological conclusions. This project strives to answer that criticism and focuses on:

1. The collection of 3D quantitative surface topographies of toolmarks by confocal microscopy;
2. Identification of relevant modern multivariate machine learning methods for tool-toolmark associations and estimations of identification error rates; and
3. Dissemination of toolmark surface data and software generated for the project to aid further research.

A database was assembled which consists of 3D striation and impression patterns on Glock fired cartridge cases, screwdriver and chisel striation patterns. The database is now available to registered users. Statistical studies were carried out on a large portion of the primer shears (cartridge cases) and screwdriver striation patterns collected thus far. Principal component analysis, canonical variate analysis and support vector machine methodology was used to objectively associate these toolmarks with the tools that created them. Estimated toolmark identification error rates were on the order of 1% using these algorithmic methods. Conformal prediction theory was used to assign confidence levels to each toolmark identification and is suggested as a useful measure in gauging the quality of a toolmark “match” for a multivariate classification system. The findings of this objective and quantitative scientific research reinforce the general conclusions codified in the AFTE theory of identification.

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