Numerical classification of curvilinear structures for the identification of pistol barrels

This paper demonstrates a numerical pattern recognition method applied to curvilinear image structures. These structures are extracted from physical cross-sections of cast internal pistol barrel surfaces. Variations in structure arise from gun design and manufacturing method providing a basis for discrimination and identification.

Binarised curvilinear land transition images are processed with fast Fourier transform on which principal component analysis is performed. One-way analysis of variance (95 % confidence interval) concludes significant differentiation between 11 barrel manufacturers when calculating weighted Euclidean distance between any trio of land transitions and an average land transition for each barrel in the database. The proposed methodology is therefore a promising novel approach for the classification and identification of firearms.