Approach to breech face impression comparison based on the robust estimation of a correspondence function

Forensic firearm analysis concerns an attempt to determine if ammunition is associated with a specific firearm based on tool-marks produced by it. A feature-based method using the Scale Invariant Feature Transform (SIFT) and RANdom SAmple Consensus (RANSAC) integration algorithm had been suggested to allow the automated comparison of breech face impressions. In this paper, an estimation method is proposed to establish a correspondence function among the features of comparison impression pairs, aiming to further improve the robustness and repeatability of automated feature matching. During the application of the iterative establishment algorithm, the Support Vector Regression (SVR) method is repeated to estimate the correspondence function based on current feature correspondences, and a robust weighting method excludes egregious outliers among putative correspondences by updating additional weightings. Moreover, the consistency detection method is adopted to overcome the over-fitting problem in SVR. Validation tests of the proposed method are conducted on three sets of cartridge case's breech face impressions; namely the Fadul set consisting of 40 cartridge cases ejected from 10 Ruger P95PR15 pistols, the Weller sets containing 95 cartridge cases obtained from 11 Ruger P95DC firearms and the Lightstone set containing 30 cartridge cases from 10 SW40VE S&W Sigma pistol slides. Test results show that most known matching (KM) pairs possess no less than 20 matching feature points while the non-matching (KNM) pairs all maintain 3–8 correspondences. It also indicates that the feature-based method has apparent advantages in dealing with granular impressions with local peaks and valleys features, and poor performance on the striation marks. The clear distinction between KM and KNM impression pairs demonstrates the feasibility of the proposed method in ballistic feature comparison. Compared to the random hypothesize-and-verify modeling of RANSAC, this method can retain more reliable matching feature points of the impression pair to ensure the repeatability of feature correspondence selection.