Analysis of Geometric Moments as Features for Identification of Forensic Ballistics Specimen
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
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When a gun is fired, characteristic markings on the cartridge and projectile of a bullet are produced. Over thirty different features can be distinguished from observing these marks, which in combination produce a "fingerprint" for identification of a firearm. In this paper, through the use of hierarchial neural networks a firearm identification system based on cartridge case images is proposed. We focus on the cartridge case identification of rim-fire mechanism. Experiments show that the model proposed has high performance and robustness by integrating two levels self-organizing feature map (SOFM) neural networks and the decision-making strategy. This model will also make a significant contribution towards the further processing, such as the more efficient and precise identification of cartridge cases by combination with more characteristics on cartridge cases images