Person Identification Method Based on PointNet++ and Adversarial Network for mmWave Radar

As a 3-D point cloud has the ability to present the contour of an object clearly, it provides more spatial information for person identification (PI) task. Aiming at the improvements on the quality of point cloud and distribution of features, an innovative treatment method for point cloud and a novel network structure are investigated in this article. First, spatiotemporal feature of point cloud is enhanced by implementing dual-stage density-based spatial clustering of applications with noise (DST-DBSCAN) method, which can filter most invalid points and decrease the sparsity of point cloud. After that, the optimized point cloud is input into neural network, which contains three parts for feature extraction, classification and feature optimization. Specifically, PointNet++ is adopted to extract features and realize PI recognition. In addition, an adversarial network is designed for optimizing feature distribution of point clouds by encouraging the feature extractor of PointNet++ to generate features of the same person as similar as possible. Experimental results demonstrate that the proposed method can improve the accuracy by 3.77% than original PointNet++ network with raw data.