Automatic firearm class identification from cartridge cases

Sridharan Kamalakannan; Christopher J. Mann; Philip R. Bingham; Thomas P. Karnowski; Shaun S. Gleason

doi:10.1117/12.872414

7 February 2011 Automatic firearm class identification from cartridge cases

Sridharan Kamalakannan, Christopher J. Mann, Philip R. Bingham, Thomas P. Karnowski, Shaun S. Gleason

Proceedings Volume 7877, Image Processing: Machine Vision Applications IV; 78770P (2011) https://doi.org/10.1117/12.872414
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

Abstract

We present a machine vision system for automatic identification of the class of firearms by extracting and analyzing two significant properties from spent cartridge cases, namely the Firing Pin Impression (FPI) and the Firing Pin Aperture Outline (FPAO). Within the framework of the proposed machine vision system, a white light interferometer is employed to image the head of the spent cartridge cases. As a first step of the algorithmic procedure, the Primer Surface Area (PSA) is detected using a circular Hough transform. Once the PSA is detected, a customized statistical region-based parametric active contour model is initialized around the center of the PSA and evolved to segment the FPI. Subsequently, the scaled version of the segmented FPI is used to initialize a customized Mumford-Shah based level set model in order to segment the FPAO. Once the shapes of FPI and FPAO are extracted, a shape-based level set method is used in order to compare these extracted shapes to an annotated dataset of FPIs and FPAOs from varied firearm types. A total of 74 cartridge case images non-uniformly distributed over five different firearms are processed using the aforementioned scheme and the promising nature of the results (95% classification accuracy) demonstrate the efficacy of the proposed approach.

Citation Download Citation

Sridharan Kamalakannan, Christopher J. Mann, Philip R. Bingham, Thomas P. Karnowski, and Shaun S. Gleason "Automatic firearm class identification from cartridge cases", Proc. SPIE 7877, Image Processing: Machine Vision Applications IV, 78770P (7 February 2011); https://doi.org/10.1117/12.872414

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