Title :
FISOFM: firearms identification based on SOFM model of neural network
Author :
Kou, Chenyuan ; Tung, Cheng-Tan ; Fu, H.C.
Author_Institution :
Inst. of Comput. Sci. & Inf. Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan
Abstract :
Firearms identification (FI) has been becoming a serious and increasing part of crime investigation for the last two decades. We propose a solution to FI using Neural Network (NN) technology. Lots of methods have been using in FI such as extractor mark, breach mark, ejector mark, and chambering mark identification, etc. We choose the chambering mark identification as our method in this research. It is a simple and useful method for crime investigation. Because of the principle of tool mark, we may identify the firearms. The chambering mark needs to be scanned, preprocessed, segmented, described, reduced and enhanced, and will be recognized by its individual characteristic via the Self-Organizing Feature Map(SOFM) model of NN. It will ease the burden of forensic laboratory´s because they do not need to identify the tool mark via microscope
Keywords :
artificial intelligence; image recognition; police data processing; self-organising feature maps; FISOFM; SOFM model; Self-Organizing Feature Map; chambering mark identification; crime investigation; firearms identification; neural network; Artificial intelligence; Pattern recognition; Self-organizing feature maps;
Conference_Titel :
Security Technology, 1994. Proceedings. Institute of Electrical and Electronics Engineers 28th Annual 1994 International Carnahan Conference on
Conference_Location :
Albuquerque, NM
Print_ISBN :
0-7803-1479-4
DOI :
10.1109/CCST.1994.363783
