Analysis of geometric moments as features for identification of forensic ballistics specimen

Nor Azura Md Ghani, Choong Yeun Liong, Abdul Aziz Jemain

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

Firearm identification is one of the most essential, intricate and demanding tasks in crime investigation. Every firearm, regardless of its size, make and model, has its own unique 'fingerprint' with respect to the marks on fired bullet and cartridge cases. In this study, we investigate the features extracted from the images of the centre of the cartridge case in which firing pin impression is located. Geometric moments up to the sixth order were computed to obtain the features based on a total of 747 cartridges case images from five different pistols of the same model. These sixteen features were found to be significantly different using the MANOVA test. Correlation analysis was used to reduce the dimensionality of the features into only six features. Classification results using cross-validation show that about 74.0% of the images were correctly classified and this demonstrates the potential of using moment based features for firearm identification.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages604-611
Number of pages8
Volume5518 LNCS
EditionPART 2
DOIs
Publication statusPublished - 2009
Event10th International Work-Conference on Artificial Neural Networks, IWANN 2009 - Salamanca
Duration: 10 Jun 200912 Jun 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5518 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other10th International Work-Conference on Artificial Neural Networks, IWANN 2009
CitySalamanca
Period10/6/0912/6/09

Fingerprint

Ballistics
Moment
Crime
Multivariate Analysis of Variance
Correlation Analysis
Fingerprint
Cross-validation
Dimensionality
Model
Demonstrate

Keywords

  • Correlation analysis
  • Discriminant analysis
  • Forensic ballistics
  • Geometric moments
  • Identification

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Ghani, N. A. M., Liong, C. Y., & Jemain, A. A. (2009). Analysis of geometric moments as features for identification of forensic ballistics specimen. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 2 ed., Vol. 5518 LNCS, pp. 604-611). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5518 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-02481-8_88

Analysis of geometric moments as features for identification of forensic ballistics specimen. / Ghani, Nor Azura Md; Liong, Choong Yeun; Jemain, Abdul Aziz.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5518 LNCS PART 2. ed. 2009. p. 604-611 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5518 LNCS, No. PART 2).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ghani, NAM, Liong, CY & Jemain, AA 2009, Analysis of geometric moments as features for identification of forensic ballistics specimen. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 edn, vol. 5518 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 5518 LNCS, pp. 604-611, 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Salamanca, 10/6/09. https://doi.org/10.1007/978-3-642-02481-8_88
Ghani NAM, Liong CY, Jemain AA. Analysis of geometric moments as features for identification of forensic ballistics specimen. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 ed. Vol. 5518 LNCS. 2009. p. 604-611. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-642-02481-8_88
Ghani, Nor Azura Md ; Liong, Choong Yeun ; Jemain, Abdul Aziz. / Analysis of geometric moments as features for identification of forensic ballistics specimen. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5518 LNCS PART 2. ed. 2009. pp. 604-611 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
@inproceedings{5da93b400f4846b68068bd1f1d0bd88c,
title = "Analysis of geometric moments as features for identification of forensic ballistics specimen",
abstract = "Firearm identification is one of the most essential, intricate and demanding tasks in crime investigation. Every firearm, regardless of its size, make and model, has its own unique 'fingerprint' with respect to the marks on fired bullet and cartridge cases. In this study, we investigate the features extracted from the images of the centre of the cartridge case in which firing pin impression is located. Geometric moments up to the sixth order were computed to obtain the features based on a total of 747 cartridges case images from five different pistols of the same model. These sixteen features were found to be significantly different using the MANOVA test. Correlation analysis was used to reduce the dimensionality of the features into only six features. Classification results using cross-validation show that about 74.0{\%} of the images were correctly classified and this demonstrates the potential of using moment based features for firearm identification.",
keywords = "Correlation analysis, Discriminant analysis, Forensic ballistics, Geometric moments, Identification",
author = "Ghani, {Nor Azura Md} and Liong, {Choong Yeun} and Jemain, {Abdul Aziz}",
year = "2009",
doi = "10.1007/978-3-642-02481-8_88",
language = "English",
isbn = "3642024807",
volume = "5518 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 2",
pages = "604--611",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
edition = "PART 2",

}

TY - GEN

T1 - Analysis of geometric moments as features for identification of forensic ballistics specimen

AU - Ghani, Nor Azura Md

AU - Liong, Choong Yeun

AU - Jemain, Abdul Aziz

PY - 2009

Y1 - 2009

N2 - Firearm identification is one of the most essential, intricate and demanding tasks in crime investigation. Every firearm, regardless of its size, make and model, has its own unique 'fingerprint' with respect to the marks on fired bullet and cartridge cases. In this study, we investigate the features extracted from the images of the centre of the cartridge case in which firing pin impression is located. Geometric moments up to the sixth order were computed to obtain the features based on a total of 747 cartridges case images from five different pistols of the same model. These sixteen features were found to be significantly different using the MANOVA test. Correlation analysis was used to reduce the dimensionality of the features into only six features. Classification results using cross-validation show that about 74.0% of the images were correctly classified and this demonstrates the potential of using moment based features for firearm identification.

AB - Firearm identification is one of the most essential, intricate and demanding tasks in crime investigation. Every firearm, regardless of its size, make and model, has its own unique 'fingerprint' with respect to the marks on fired bullet and cartridge cases. In this study, we investigate the features extracted from the images of the centre of the cartridge case in which firing pin impression is located. Geometric moments up to the sixth order were computed to obtain the features based on a total of 747 cartridges case images from five different pistols of the same model. These sixteen features were found to be significantly different using the MANOVA test. Correlation analysis was used to reduce the dimensionality of the features into only six features. Classification results using cross-validation show that about 74.0% of the images were correctly classified and this demonstrates the potential of using moment based features for firearm identification.

KW - Correlation analysis

KW - Discriminant analysis

KW - Forensic ballistics

KW - Geometric moments

KW - Identification

UR - http://www.scopus.com/inward/record.url?scp=77952561820&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77952561820&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-02481-8_88

DO - 10.1007/978-3-642-02481-8_88

M3 - Conference contribution

AN - SCOPUS:77952561820

SN - 3642024807

SN - 9783642024801

VL - 5518 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 604

EP - 611

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -