Millimeter-Wave Person Recognition System using Hybrid Features and SVM Classification

Human Recognition is hastily improving in day-to-day life. Digital Image Processing (DIP) is a rapidly evolving field with blooming applications in Science and Engineering. The accuracy of human recognition system is mostly affected by varying lighting conditions. Due to the ability of millimeter waves (mmWs) to penetrate dielectric materials, such as plastic, polymer, and clothes, the mmW imaging technology has been widely used for the detection of concealed weapons and objects.The use of mmW images has also recently been proposed for biometric person recognition to overcome certain limitations in image acquisition at visible frequencies. The biometric recognition module of this work aims to perform person recognition through body shape-based and texture information extracted from GLCM using SVM classification. This project proposes a biometric person recognition system based on the shape information extracted from mmW images and texture feature extraction using Gray Level Co-occurrence (GLCM) technique is proposed. Extracted features are applied to SVM Classification technique for accurate person recognition from millimeter wave images. Experimental results suggest the potential of performing person recognition through mmW imaging using only shape information, a functionality that could be integrated in the security scanners deployed in airports.