Citation

Zhang H, Zhu J, Hong R, Wang H, Sun F, Malik A. J. Forensic Sci. 2020; ePub(ePub): ePub.

Copyright

(Copyright © 2020, American Society for Testing and Materials, Publisher John Wiley and Sons)

DOI

10.1111/1556-4029.14634

PMID

33227148

Abstract

A firing pin impression is usually concave in shape with a small textured area, which makes it difficult to perform automated algorithm-based comparison. The congruent matching cells (CMC) method was invented for accurate breech face impression comparison, in which a reference impression is divided into correlation cells. Each cell is registered to a cell-sized area of the comparison impression that has maximum similarity in surface topography. Four parameters are used to quantify the congruent matching pattern of the registration position and orientation. This paper aims to further develop the cell-division-matching method based on a convergence feature and to develop practical convergence-improved algorithms for firing pin impression comparison. The convergence feature refers to the tendency of the x-y registration positions of correlated cell pairs to converge at the correct registration angle when comparing same-source samples at different orientations. The areal Gaussian filter is employed to extract high-frequency micro-features; the least-squares matching method is used to improve each cross-correlation precision and reach convergence in the registration positions of correlated cell pairs; and a density-based clustering algorithm is introduced to collect the registration positions of dense cell pairs relative to a virtual common center and to remove outliers. Improvements are achieved in the reliability and accuracy of the number of congruent matching cell pairs (CMCs) collected, which represents the quantification of the degree of pairwise impression similarity. Experiments in this report used 40 firing pin impression samples on cartridge cases fired from 10 pistols. The results included no false identifications or false exclusions.

Language: en

Keywords

automated ballistic identification; congruent matching cells; density-based clustering; firing pin impression; least-square matching