A robust firearm identification algorithm of forensic ballistics specimens

There are several inherent difficulties in the existing firearm identification algorithms, include requiring the physical interpretation and time consuming. Therefore, the aim of this study is to propose a robust algorithm for a firearm identification based on extracting a set of informative features from the segmented region of interest (ROI) using the simulated noisy center-firing pin impression images. The proposed algorithm comprises Laplacian sharpening filter, clustering-based threshold selection, unweighted least square estimator, and segment a square ROI from the noisy images. A total of 250 simulated noisy images collected from five different pistols of the same make, model and caliber are used to evaluate the robustness of the proposed algorithm. This study found that the proposed algorithm is able to perform the identical task on the noisy images with noise levels as high as 70%, while maintaining a firearm identification accuracy rate of over 90%.

[1]  Zhonghua Lin,et al.  The Pupil Location Based on the OTSU Method and Hough Transform , 2011 .

[2]  Raymond H. Chan,et al.  A Detection Statistic for Random-Valued Impulse Noise , 2007, IEEE Transactions on Image Processing.

[3]  Josef Kittler,et al.  A Comparative Study of Hough Transform Methods for Circle Finding , 1989, Alvey Vision Conference.

[4]  Jan De Kinder,et al.  Reference ballistic imaging database performance. , 2004, Forensic science international.

[5]  D. G. Li.  Image processing for the positive identification of forensic ballistics specimens , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[6]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[7]  Frank Y. Shih,et al.  Image Processing and Pattern Recognition: Fundamentals and Techniques , 2010 .

[8]  J. Kittler,et al.  Comparative study of Hough Transform methods for circle finding , 1990, Image Vis. Comput..

[9]  Thomas S. Huang,et al.  The Effect of Median Filtering on Edge Estimation and Detection , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Raymond H. Chan,et al.  Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization , 2005, IEEE Transactions on Image Processing.

[11]  Andrew W. Fitzgibbon,et al.  Direct Least Square Fitting of Ellipses , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Gang Rong,et al.  A cartridge identification system for firearm authentication , 2000, WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000.

[13]  Abdul Aziz Jemain,et al.  Pendekatan Pengesanan Titik Sauh Secara Automatik bagi Kesan Pin Peletup Senjata Api , 2013 .

[14]  Richard I. Kitney,et al.  A direct method for least-squares circle fitting , 1991 .

[15]  Raj Gururajan,et al.  A de-noising method for heart sound signal using Otsu's threshold selection , 2011 .

[16]  David Zhang,et al.  Automated cartridge identification for firearm authentication , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[17]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[18]  Maya R. Gupta,et al.  OCR binarization and image pre-processing for searching historical documents , 2007, Pattern Recognit..

[19]  Saadi Bin Ahmad Kamaruddin,et al.  Firearm recognition based on whole firing pin impression image via backpropagation neural network , 2011, 2011 International Conference on Pattern Analysis and Intelligence Robotics.

[20]  Charles K. Chui,et al.  A universal noise removal algorithm with an impulse detector , 2005, IEEE Transactions on Image Processing.

[21]  Antonio Albano,et al.  Representation of Digitized Contours in Terms of Conic Arcs and Straight-Line Segments , 1974, Comput. Graph. Image Process..

[22]  Andrew W. Fitzgibbon,et al.  Direct least squares fitting of ellipses , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[23]  Jinsong Leng,et al.  On analysis of circle moments and texture features for cartridge images recognition , 2012, Expert Syst. Appl..

[24]  Abdul Aziz Jemain,et al.  Analysis of geometric moments as features for firearm identification. , 2010, Forensic science international.

[25]  D. Ebenezer,et al.  New Nonlinear Filtering Strategies for Eliminating Medium and Long Tailed Noise in Images with Edge Preservation Properties , 2005 .

[26]  J Bijhold,et al.  Image matching algorithms for breech face marks and firing pins in a database of spent cartridge cases of firearms. , 2001, Forensic science international.