Gun detection and classification based on feature extraction from a new sensor array imaging system

Abstract

Electromagnetic imaging currently occupies a vital role in various disciplines from engineering to medical applications. These roles are based upon the fundamentals of Electromagnetic (EM) fields and their relationship with the material properties under evaluation. A new system based on a Giant Magneto-Resistive (GMR) sensor array was built to capture the scattered EM signal returned by metallic objects. This paper evaluates the capabilities of the new system based on features extracted from objects response to EM fields. A novel amplitude variation feature is proposed to obtain high classification rates. The selected features of metallic objects are applied to detect and classify `threat' items. A collection of handguns with other commonly used metallic objects are tested. Promising results show that the new system can detect and identify the threat items. This novel procedure has the potential to produce significant improvements in automatic weapon detection and classification.