A Convergence Algorithm for Correlation of Breech Face Images Based on the Congruent Matching Cells (CMC) Method

Published

November 6, 2017

Author(s)

Zhe Chen, Jun-Feng Song, Wei Chu, Johannes A. Soons, Xuezeng Zhao

Abstract

The Congruent Matching Cells (CMC) method was invented at the National Institute of Standards and Technology (NIST) for accurate firearm evidence identification and error rate estimation. The CMC method is based on the principle of discretization. The compared image pairs are divided into cells, and four parameters are used to quantify topography similarity and pattern congruency of the correlated cell pairs. The use of cell correlations reduces the effects of "invalid regions" in the compared image pairs and increases the correlation accuracy. The CMC method has been tested by correlations of both 3D topography images and 2D optical images of the breech face impressions of 40 cartridge cases fired from guns with 10 consecutively manufactured pistol slides. The identification accuracy of the CMC method can be further improved by a newly discovered feature named "convergence", that is, the tendency of the x-y registration positions of the correlated cell pairs to converge at the correct registration angle when comparing same-source samples. In this paper, the difference of the convergence feature between known matching (KM) and known non-matching (KNM) image pairs is characterized, based on which an improved algorithm is developed for breech face image correlations using the CMC method. Its advantage is demonstrated by comparison with existing algorithms using four datasets.

Citation

Forensic Science International

Volume

280

Pub Type

Journals

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DOI Link

Keywords

forensics, ballistics, firearm evidence identification, cartridge case, Congruent Matching Cells (CMC), convergence.

Citation

Chen, Z. , Song, J. , Chu, W. , Soons, J. and Zhao, X. (2017), A Convergence Algorithm for Correlation of Breech Face Images Based on the Congruent Matching Cells (CMC) Method, Forensic Science International, [online], https://doi.org/10.1016/j.forsciint.2017.08.033 (Accessed October 3, 2024)