Conceptual design of firearm identification mobile application (FIMA)

Liong, Choong Yeun; Ghani, Nor Azura Md; Kamaruddin, Saadi Bin Ahmad; Jemain, Abdul Aziz

doi:10.1063/5.0018445

Cited by 1 publication

(2 citation statements)

References 11 publications

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“…Recently, Liong et al [25] carried out another research on developing a mobile application for a ballistics identification algorithm. In this study, they have carried out a comparison of the effectiveness of the selected geometric moments feature set, which was also extracted from three distinctive segmented centre-firing pin impression images as the research works of Ghani et al [2,3,4,15].…”

Section: Related Workmentioning

confidence: 99%

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A Comparative of Two-Dimensional Statistical Moment Invariants Features in Formulating an Automated Probabilistic Machine Learning Identification Algorithm for Forensic Application

Chuan,

Chong Teak Wei,

Lee Wai Yan

et al. 2023

Mal. J. Fund. Appl. Sci.

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IBIS, ALIS, EVOFINDER, and CONDOR are the massive ballistics computerised technological machines that have typically been utilised in forensic laboratories to automatically locate similarities between images of cartridge cases and bullets. However, it imposed a long execution time and requires physical interpretation to consolidate the analysis results when employing these market-available technologies to accomplish ballistics matching tasks. Therefore, the principal objective of this study is to propose an improvised automated probabilistic machine learning identification algorithm by extracting the two-dimensional (2D) statistical moment invariants from the segmented region of interest (ROI) corresponding to the cartridge case and bullets images. To pursue this principal objective, several 2D statistical moment invariants have been compared and tested to determine the most suitable feature set applied in the proposed identification algorithm. The 2D statistical moment invariants employed include Orthogonal Legendre moments (OLM), Hu moments (HM), Tsirikolias-Mertzois moments (TMM), Pan-Keane moments (PKM), and Central Geometric moments (CGM). Moreover, the proposed identification algorithm is also tested in different scenarios, including based on the classification of strength association measurements between the extracted feature sets. The empirical results in this article revealed that the proposed identification algorithm applied with the CGM comprising the weak association classification yielded the best identification accuracy rates, which are >96.5% across all the sample sizes of the training set. These empirical results also conveyed that the superior proposed identification algorithm in this research could be developed as a mobile application for ballistics identification that can significantly reduce the time taken and conveniently perform the ballistics identification tasks.

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Section: Related Workmentioning

confidence: 99%

“…The limitation of the extraction of geometric moments as the feature set in the previously proposed machine learning identification algorithms for ballistics [2,4,11,14,15,20,25] is non-orthogonal moments so that the extracted moments do not comprise the invariant properties corresponding to TRS.…”

Section: Central Geometric Moment Invariantsmentioning

confidence: 99%