Fast, Fully-Automatic Characterization of Metallic and Water-Based Threat Objects for Millimeter-Wave Personnel Screening Systems

Efficient characterization of concealed person-worn objects enhances the security of air travel and reduces the inspection time. Mm-wave nearfield radar can detect metallic objects such as guns or knives, as well as water-based materials that might be associated with peroxide threats. However, it cannot discriminate these potential threats from benign objects to decrease the nuisance alarm rate. Moreover, distinguishing these potential threats from benign objects in the presence of human skin is even more challenging. The authors have previously developed a method using mm-wave imaging to estimate the nominal body contour (NBC) with attached objects. This paper extends the image-processing-based algorithm to identify concealed metallic and water-based objects, as thin as 1 cm, attached to arms, legs, torso, and the pelvic region. The algorithm determines if the anomaly in the image is due to lossless or conductive material seen respectively as a depression or protrusion relative to the NBC. Next, high-loss water-based objects are distinguished from metallic and benign low-loss objects. The developed method is verified experimentally via actual images of human subjects, with foreign attached objects of various types and sizes. The images were captured by the High Definition-Advanced Imaging Technology (HD-AIT) scanner, a laboratory prototype system developed recently by the U.S. Department of Homeland Security (DHS). The developed codes resulted in zero miss and 7% rate of false alarm, while the rest of objects were either characterized correctly or referred to for a secondary pat down. Our method is performed fully-automatically and within a few seconds.