A Firearm Identification System Based on Neural Network
Faculty of Computing, Health and Science
School of Computer and Information Science
In this paper, a Firearm Identification system based on Self-Organizing Feature Map (SOFM) neural network is proposed. We focus on the cartridge case identification of rim-firing mechanism. Experiments show that the model proposed has high performance and robustness by integrating the SOFM neural network 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.