Bullet matching using SIFT feature

Abstract

Firearms leave special marks on the bullet while the bullet travels through the barrel. In this work, visual word codes obtained from interest points were used in bullet matching. Visual codebook was constructed by clustering Scale Invariant Feature Transform (SIFT) features using interest point orientation information as semi-supervised clustering constraint. The ratio of the number of visual words in common to the total number of visual words was used as a similarity metric in the comparison of images. Visual words are weighted by inverse document frequency which is frequently used in text document comparisons. Experiment results show that the proposed method presents promising results in bullet matching.