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

fYear

1994

fDate

12-14 Oct 1994

Firstpage

120

Lastpage

125

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/CCST.1994.363783

Filename

363783