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.
"The classification results using leave-one-out cross-validation shows that overall 85.2% of the images of the cartridge cases were correctly classified according to the types of pistol used. The classification rates using PCA are better compared to those using only purely statistical  or geometric moment features . Table 3 presents the detailed classification rates using cross-validation. "
[Show abstract][Hide abstract] ABSTRACT: Many heavy crimes committed such as murders or robberies frequently involve firearms, particularly pistols. In order to solve the crime cases, firearm identification is becoming vital. Unique marks are left on the bullet and the cartridge case when a firearm is fired. The firing pin impression is one of the most vital marks on any cartridge case. In this study, a total of 68 features of firing pin impression images – 20 basic statistical features, and 48 geometric moment features up to the sixth order – were extracted from three regions of the firing pin impression image, namely whole, centre and ring images. Five different types of pistol of the Parabellum Vector SPI 9 mm model were tested, where 50 bullets were fired from each pistol. Preliminary analysis using Pearson correlation shows that the features are significantly highly correlated. Therefore principal component analysis (PCA) was used to analyze the interrelationship among the features and combine them into a smaller set of factors while maintaining maximum information of the original patterns. PCA has reduced the dimensionality of the features into nine significant components of features. Discriminant analysis was used to identify the types of pistols used based on the new components. A total of 85.2% of the images were correctly classified according to the pistols used using cross-validation under discriminant analysis. The result demonstrates the potential of using PCA to reduce the dimensions of the numerical features towards an efficient firearm identification system.
[Show abstract][Hide abstract] ABSTRACT: To enable efficient resource provisioning in HaaS (Hardware as a Service) cloud systems, virtual machine packing, which migrate
virtual machines to minimize running real node, is essential. The virtual machine packing problem is a multi-objective optimization
problem with several parameters and weights on parameters change dynamically subject to cloud provider preference. We propose
to employ Genetic Algorithm (GA) method, that is one of the meta-heuristics. We implemented a prototype Virtual Machine packing
optimization mechanism on Grivon, which is a virtual cluster management system we have been developing. The preliminary evaluation
implied the GA method is promising for the problem.
Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, 10th International Work-Conference on Artificial Neural Networks, IWANN 2009 Workshops, Salamanca, Spain, June 10-12, 2009. Proceedings, Part II; 01/2009
[Show abstract][Hide abstract] ABSTRACT: There are many crime cases such as murders or robberies which frequently involve firearms, especially pistols. The centre firing pin impression image on a cartridge case is one of the important clues for firearms identification. In this study, a total of 16 features of geometric moments up to the sixth order were extracted from centre of firing pin impression images. A total of five pistols of the Parabellum Vector SPI 9mm model, made in South Africa were used. The pistols were labelled as Pistol A, Pistol B, Pistol C, Pistol D, and Pistol E. A total of 747 bullets have been fired from the five pistols. Under preliminary analysis, Pearson correlation coefficients between all pairs of features showed the features were significant and highly correlated among the features. This problematic features were solved by dividing the features into subgroups of variables based on similar characteristics under principle component analysis. The features that highly correlated were combined into meaningful components or factors. Discriminant analysis was applied to identify the types of pistols used based on the factors obtained. Classification results using cross-validation under discriminant analysis showed that 75.4% of the images were correctly classified according to the pistols used. The results of the study had shown a significant contribution towards Royal Malaysian Police Force in handling crime cases which involve firearms in more systematic manner.
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