Treffer: Classification of firing pin impressions using HOG-SVM.
Title:
Classification of firing pin impressions using HOG-SVM.
Authors:
Wen Z; Institute of Environmental Science and Research Limited, Auckland, New Zealand., Curran JM; Institute of Environmental Science and Research Limited, Auckland, New Zealand., Harbison S; Institute of Environmental Science and Research Limited, Auckland, New Zealand.; Department of Statistics, University of Auckland, Auckland, New Zealand., Wevers GE; Department of Statistics, University of Auckland, Auckland, New Zealand.
Source:
Journal of forensic sciences [J Forensic Sci] 2023 Nov; Vol. 68 (6), pp. 1946-1957. Date of Electronic Publication: 2023 Sep 10.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Blackwell Pub Country of Publication: United States NLM ID: 0375370 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1556-4029 (Electronic) Linking ISSN: 00221198 NLM ISO Abbreviation: J Forensic Sci Subsets: PubMed not MEDLINE; MEDLINE
Imprint Name(s):
Publication: 2006- : Malden, MA : Blackwell Pub.
Original Publication: [Chicago, Ill.] : Callaghan and Co., 1956-
Original Publication: [Chicago, Ill.] : Callaghan and Co., 1956-
References:
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Thompson RM. Automated firearms evidence comparison using the integrated ballistic identification system (IBIS). In: Higgins K, editor. Proceedings Volume 3576 - Investigation and Forensic Science Technologies; 1998 Nov 3-4; Boston, MA. Bellingham, WA: SPIE; 1999. p. 94-103. https://doi.org/10.1117/12.334519.
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Song J. Proposed NIST ballistics identification system (NBIS) based on 3D topography measurements on correlation cells. AFTE J. 2013;45(2):184-194.
Chu W, Tong M, Song J-F. Validation tests for the congruent matching cells (CMC) method using cartridge cases fired with consecutively manufactured pistol slides. AFTE J. 2013;45(2):361-366.
Song J, Vorburger TV, Chu W, Yen J, Soons JA, Ott DB, et al. Estimating error rates for firearm evidence identifications in forensic science. Forensic Sci Int. 2018;284:15-32. https://doi.org/10.1016/j.forsciint.2017.12.013.
Tong M, Yu X, Huang S. Automatic identification of firing pin impressions based on the congruent matching cell (CMC) method. Neurocomputing. 2019;367:246-258. https://doi.org/10.1016/j.neucom.2019.08.033.
Tong M, Song J, Chu W. An improved algorithm of congruent matching cells (CMC) method for firearm evidence identifications. J Res Natl Inst Stand Technol. 2015;120:102-112. https://doi.org/10.6028/jres.120.008.
Tai XH, Eddy WF. A fully automatic method for comparing cartridge case images. J Forensic Sci. 2018;63(2):440-448. https://doi.org/10.1111/1556-4029.13577.
Azad P, Asfour T, Dillmann R. Combining Harris interest points and the sift descriptor for fast scale-invariant object recognition. Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems; 2009 Oct 11-15; St. Louis, MO. Piscataway, NJ: IEEE; 2009. p. 4275-4280. https://doi.org/10.1109/IROS.2009.5354611.
Geng C, Jiang X. Face recognition using sift features. Proceedings of the 2009 16th IEEE International Conference on Image Processing (ICIP); 2009 Nov 7-10; Cairo, Egypt. Piscataway, NJ: IEEE; 2009. p. 3313-3316. https://doi.org/10.1109/ICIP.2009.5413956.
Moranduzzo T, Melgani F. A sift-svm method for detecting cars in uav images. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium; 2012 July 22-27; Munich, Germany. Piscataway, NJ: IEEE; 2012. p. 6868-6871. https://doi.org/10.1109/IGARSS.2012.6352585.
Zhuo L, Geng Z, Zhang J, Li XG. ORB feature based web pornographic image recognition. Neurocomputing. 2016;173:511-517. https://doi.org/10.1016/j.neucom.2015.06.055.
Biasotti AA. A statistical study of the individual characteristics of fired bullets. J Forensic Sci. 1959;4(1):34-50.
Faden D, Kidd J, Craft J, Chumbley L, Morris M, Genalo L, et al. Statistical confirmation of empirical observations concerning tool mark striae. AFTE J. 2007;39(3):205-214.
Mattijssen EJ, Witteman CL, Berger CE, Brand NW, Stoel RD. Validity and reliability of forensic firearm examiners. Forensic Sci Int. 2020;307:110112. https://doi.org/10.1016/j.forsciint.2019.110112.
Baiker M, Petraco ND, Gambino C, Pieterman R, Shenkin P, Zoon P. Virtual and simulated striated toolmarks for forensic applications. Forensic Sci Int. 2016;261:43-52. https://doi.org/10.1016/j.forsciint.2016.01.035.
Gambino C, McLaughlin P, Kuo L, Kammerman F, Shenkin P, Diaczuk P, et al. Forensic surface metrology: tool mark evidence. Scanning. 2011;33(5):272-278. https://doi.org/10.1002/sca.20251.
Ghani NAM, Liong C-Y, Jemain AA. Analysis of geometric moments as features for firearm identification. Forensic Sci Int. 2010;198(1-3):143-149. https://doi.org/10.1016/j.forsciint.2010.02.011.
Zhou J, Xin L-P, Rong G, Zhang D. Decision fusion based cartridge identification using support vector machine. Smc 2000 conference proceedings. Proceedings of the 2000 IEEE International Conference on Systems, Man and Cybernetics; 2000 Oct 8-11; Nashville, TN. Piscataway, NJ: IEEE; 2000. p. 2873-2877. https://doi.org/10.1109/ICSMC.2000.884434.
Duda RO, Hart PE. Use of the hough transformation to detect lines and curves in pictures. Commun ACM. 1972;15(1):11-15. https://doi.org/10.1145/361237.361242.
Trahanias PE, Venetsanopoulos AN. Color image enhancement through 3-d histogram equalization. Proceedings of the 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis; 1992 Aug 30-Sept 1; The Hague, Netherldands. Piscataway, NJ: IEEE; 1992. p. 545-548. https://doi.org/10.1109/ICPR.1992.202045.
Garg P, Jain T. A comparative study on histogram equalization and cumulative histogram equalization. Int J New Tech Res. 2017;3(9):263242.
Ahmad M, Sundararajan D. A fast algorithm for two dimensional median filtering. IEEE Trans Circuits Syst. 1987;34(11):1364-1374. https://doi.org/10.1109/TCS.1987.1086059.
Schalkoff RJ. Digital image processing and computer vision: an introduction to theory and implementations. Hoboken, NJ: John Wiley & Sons, Inc.; 1989.
Dalal N, Triggs B. Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05); 2005 June 20-26; San Diego, CA. Piscataway, NJ: IEEE; 2005. p. 886-893. https://doi.org/10.1109/CVPR.2005.177.
Li X-Y, Lin Z-X. Face recognition based on hog and fast PCA algorithm. In: Kromer P, Alba E, Pan J-S, Snasel V, editors. Proceedings of the fourth euro-China conference on intelligent data analysis and applications; 2017 oct 9-11. Bulevar Louis Pasteur, Spaon: Springer; 2017. p. 10-21. https://doi.org/10.1007/978-3-319-68527-4_2.
Choudhury A, Rana HS, Bhowmik T. Handwritten bengali numeral recognition using HOG based feature extraction algorithm. Proceedings of the 5th International Conference on Signal Processing and Integrated Networks (SPIN 2018); 2018 Feb 22-23; Noida, Delhi-NCR, India. Piscataway, NJ: IEEE; 2018. p. 687-690. https://doi.org/10.1109/SPIN.2018.8474215.
Dubey AR, Shukla N, Kumar D. Detection and classification of road signs using HOG-SVM method. In: Elci A, Kumar Sa P, Modi CN, Olague G, Sahoo MN, Bakshi S, editors. Smart computing paradigms: new progresses and challenges. Singapore: Springer; 2020. p. 49-56. https://doi.org/10.1007/978-981-13-9683-0_6.
Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Series B Stat Methodol. 1996;58(1):267-288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x.
Lowe DG. Object recognition from local scale-invariant features. Proceedings of the Seventh IEEE International Conference on Computer Vision; 1999 Sept 20-27; Corfu, Greece. Piscataway, NJ: IEEE; 1999. p. 1150-1157. https://doi.org/10.1109/ICCV.1999.790410.
Bay H, Ess A, Tuytelaars T, Van Gool L. Speeded-up robust features (SURF). Comput Vis Image Underst. 2008;110(3):346-359. https://doi.org/10.1007/11744023_32.
Rublee E, Rabaud V, Konolige K, Bradski G. ORB: an efficient alternative to sift or surf. In: Crowley JL, Draper B, Thonnat M, editors. Proceedings of the 2011 International Conference on Computer Vision; 2011 Sept 20-22; Sophia Antipolis, France. Piscataway, NJ: IEEE; 2011. p. 2564-2571. https://doi.org/10.1109/ICCV.2011.6126544.
Calonder M, Lepetit V, Strecha C, Fua P. Brief: binary robust independent elementary features. In: Daniilidis K, Maragos P, Paragios N, editors. Proceedings of the 11th European Conference on Computer Vision (ECCV 2010); 2010 Sept 5-11; Heraklion, Crete, Greece. Berlin/Heidelberg, Germany: Springer; 2010. p. 778-792. https://doi.org/10.1007/978-3-642-15561-1_56.
Van Rossum G, Drake FL. Python 3 reference manual. Scotts Valley, CA: CreateSpace; 2009.
Bradski G. The openCV library. Dr Dobb's J. 2000;25(11):120-123.
Murdock J, Cavallo J, Kreiser J, Meyers C, Morris B, Sibert B, et al. Theory of identification, range of striae comparison reports and modified glossary definitions-an AFTE criteria for identification committee report. AFTE J. 1990;22(3):275-279.
Thompson RM. Automated firearms evidence comparison using the integrated ballistic identification system (IBIS). In: Higgins K, editor. Proceedings Volume 3576 - Investigation and Forensic Science Technologies; 1998 Nov 3-4; Boston, MA. Bellingham, WA: SPIE; 1999. p. 94-103. https://doi.org/10.1117/12.334519.
Smith CL. Fireball: a forensic ballistics imaging system. Proceedings of the IEEE 31st Annual 1997 International Carnahan Conference on Security Technology; 1997 Oct 15-17; Canberra, Australia. Piscataway, NJ: IEEE; 1997. p. 64-70. https://doi.org/10.1109/CCST.1997.626240.
Song J. Proposed NIST ballistics identification system (NBIS) based on 3D topography measurements on correlation cells. AFTE J. 2013;45(2):184-194.
Chu W, Tong M, Song J-F. Validation tests for the congruent matching cells (CMC) method using cartridge cases fired with consecutively manufactured pistol slides. AFTE J. 2013;45(2):361-366.
Song J, Vorburger TV, Chu W, Yen J, Soons JA, Ott DB, et al. Estimating error rates for firearm evidence identifications in forensic science. Forensic Sci Int. 2018;284:15-32. https://doi.org/10.1016/j.forsciint.2017.12.013.
Tong M, Yu X, Huang S. Automatic identification of firing pin impressions based on the congruent matching cell (CMC) method. Neurocomputing. 2019;367:246-258. https://doi.org/10.1016/j.neucom.2019.08.033.
Tong M, Song J, Chu W. An improved algorithm of congruent matching cells (CMC) method for firearm evidence identifications. J Res Natl Inst Stand Technol. 2015;120:102-112. https://doi.org/10.6028/jres.120.008.
Tai XH, Eddy WF. A fully automatic method for comparing cartridge case images. J Forensic Sci. 2018;63(2):440-448. https://doi.org/10.1111/1556-4029.13577.
Azad P, Asfour T, Dillmann R. Combining Harris interest points and the sift descriptor for fast scale-invariant object recognition. Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems; 2009 Oct 11-15; St. Louis, MO. Piscataway, NJ: IEEE; 2009. p. 4275-4280. https://doi.org/10.1109/IROS.2009.5354611.
Geng C, Jiang X. Face recognition using sift features. Proceedings of the 2009 16th IEEE International Conference on Image Processing (ICIP); 2009 Nov 7-10; Cairo, Egypt. Piscataway, NJ: IEEE; 2009. p. 3313-3316. https://doi.org/10.1109/ICIP.2009.5413956.
Moranduzzo T, Melgani F. A sift-svm method for detecting cars in uav images. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium; 2012 July 22-27; Munich, Germany. Piscataway, NJ: IEEE; 2012. p. 6868-6871. https://doi.org/10.1109/IGARSS.2012.6352585.
Zhuo L, Geng Z, Zhang J, Li XG. ORB feature based web pornographic image recognition. Neurocomputing. 2016;173:511-517. https://doi.org/10.1016/j.neucom.2015.06.055.
Biasotti AA. A statistical study of the individual characteristics of fired bullets. J Forensic Sci. 1959;4(1):34-50.
Faden D, Kidd J, Craft J, Chumbley L, Morris M, Genalo L, et al. Statistical confirmation of empirical observations concerning tool mark striae. AFTE J. 2007;39(3):205-214.
Mattijssen EJ, Witteman CL, Berger CE, Brand NW, Stoel RD. Validity and reliability of forensic firearm examiners. Forensic Sci Int. 2020;307:110112. https://doi.org/10.1016/j.forsciint.2019.110112.
Baiker M, Petraco ND, Gambino C, Pieterman R, Shenkin P, Zoon P. Virtual and simulated striated toolmarks for forensic applications. Forensic Sci Int. 2016;261:43-52. https://doi.org/10.1016/j.forsciint.2016.01.035.
Gambino C, McLaughlin P, Kuo L, Kammerman F, Shenkin P, Diaczuk P, et al. Forensic surface metrology: tool mark evidence. Scanning. 2011;33(5):272-278. https://doi.org/10.1002/sca.20251.
Ghani NAM, Liong C-Y, Jemain AA. Analysis of geometric moments as features for firearm identification. Forensic Sci Int. 2010;198(1-3):143-149. https://doi.org/10.1016/j.forsciint.2010.02.011.
Zhou J, Xin L-P, Rong G, Zhang D. Decision fusion based cartridge identification using support vector machine. Smc 2000 conference proceedings. Proceedings of the 2000 IEEE International Conference on Systems, Man and Cybernetics; 2000 Oct 8-11; Nashville, TN. Piscataway, NJ: IEEE; 2000. p. 2873-2877. https://doi.org/10.1109/ICSMC.2000.884434.
Duda RO, Hart PE. Use of the hough transformation to detect lines and curves in pictures. Commun ACM. 1972;15(1):11-15. https://doi.org/10.1145/361237.361242.
Trahanias PE, Venetsanopoulos AN. Color image enhancement through 3-d histogram equalization. Proceedings of the 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis; 1992 Aug 30-Sept 1; The Hague, Netherldands. Piscataway, NJ: IEEE; 1992. p. 545-548. https://doi.org/10.1109/ICPR.1992.202045.
Garg P, Jain T. A comparative study on histogram equalization and cumulative histogram equalization. Int J New Tech Res. 2017;3(9):263242.
Ahmad M, Sundararajan D. A fast algorithm for two dimensional median filtering. IEEE Trans Circuits Syst. 1987;34(11):1364-1374. https://doi.org/10.1109/TCS.1987.1086059.
Schalkoff RJ. Digital image processing and computer vision: an introduction to theory and implementations. Hoboken, NJ: John Wiley & Sons, Inc.; 1989.
Dalal N, Triggs B. Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05); 2005 June 20-26; San Diego, CA. Piscataway, NJ: IEEE; 2005. p. 886-893. https://doi.org/10.1109/CVPR.2005.177.
Li X-Y, Lin Z-X. Face recognition based on hog and fast PCA algorithm. In: Kromer P, Alba E, Pan J-S, Snasel V, editors. Proceedings of the fourth euro-China conference on intelligent data analysis and applications; 2017 oct 9-11. Bulevar Louis Pasteur, Spaon: Springer; 2017. p. 10-21. https://doi.org/10.1007/978-3-319-68527-4_2.
Choudhury A, Rana HS, Bhowmik T. Handwritten bengali numeral recognition using HOG based feature extraction algorithm. Proceedings of the 5th International Conference on Signal Processing and Integrated Networks (SPIN 2018); 2018 Feb 22-23; Noida, Delhi-NCR, India. Piscataway, NJ: IEEE; 2018. p. 687-690. https://doi.org/10.1109/SPIN.2018.8474215.
Dubey AR, Shukla N, Kumar D. Detection and classification of road signs using HOG-SVM method. In: Elci A, Kumar Sa P, Modi CN, Olague G, Sahoo MN, Bakshi S, editors. Smart computing paradigms: new progresses and challenges. Singapore: Springer; 2020. p. 49-56. https://doi.org/10.1007/978-981-13-9683-0_6.
Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Series B Stat Methodol. 1996;58(1):267-288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x.
Lowe DG. Object recognition from local scale-invariant features. Proceedings of the Seventh IEEE International Conference on Computer Vision; 1999 Sept 20-27; Corfu, Greece. Piscataway, NJ: IEEE; 1999. p. 1150-1157. https://doi.org/10.1109/ICCV.1999.790410.
Bay H, Ess A, Tuytelaars T, Van Gool L. Speeded-up robust features (SURF). Comput Vis Image Underst. 2008;110(3):346-359. https://doi.org/10.1007/11744023_32.
Rublee E, Rabaud V, Konolige K, Bradski G. ORB: an efficient alternative to sift or surf. In: Crowley JL, Draper B, Thonnat M, editors. Proceedings of the 2011 International Conference on Computer Vision; 2011 Sept 20-22; Sophia Antipolis, France. Piscataway, NJ: IEEE; 2011. p. 2564-2571. https://doi.org/10.1109/ICCV.2011.6126544.
Calonder M, Lepetit V, Strecha C, Fua P. Brief: binary robust independent elementary features. In: Daniilidis K, Maragos P, Paragios N, editors. Proceedings of the 11th European Conference on Computer Vision (ECCV 2010); 2010 Sept 5-11; Heraklion, Crete, Greece. Berlin/Heidelberg, Germany: Springer; 2010. p. 778-792. https://doi.org/10.1007/978-3-642-15561-1_56.
Van Rossum G, Drake FL. Python 3 reference manual. Scotts Valley, CA: CreateSpace; 2009.
Bradski G. The openCV library. Dr Dobb's J. 2000;25(11):120-123.
Contributed Indexing:
Keywords: computer vision; firing pin impression; histogram of orientated gradient; image classification; support vector machine
Entry Date(s):
Date Created: 20230911 Latest Revision: 20231026
Update Code:
20250114
DOI:
10.1111/1556-4029.15377
PMID:
37691406
Database:
MEDLINE
Weitere Informationen
Crimes, such as robbery and murder, often involve firearms. In order to assist with the investigation into the crime, firearm examiners are asked to determine whether cartridge cases found at a crime scene had been fired from a suspect's firearm. This examination is based on a comparison of the marks left on the surfaces of cartridge cases. Firing pin impressions can be one of the most commonly used of these marks. In this study, a total of nine Ruger model 10/22 semiautomatic rifles were used. Fifty cartridges were fired from each rifle. The cartridge cases were collected, and each firing pin impression was then cast and photographed using a comparison microscope. In this paper, we will describe how one may use a computer vision algorithm, the Histogram of Orientated Gradient (HOG), and a machine learning method, Support Vector Machines (SVMs), to classify images of firing pin impressions. Our method achieved a reasonably high accuracy at 93%. This can be used to associate a firearm with a cartridge case recovered from a scene. We also compared our method with other feature extraction algorithms. The comparison results showed that the HOG-SVM method had the highest performance in this classification task.
(© 2023 American Academy of Forensic Sciences.)