  Achieving accurate human identification through RF imaging has been a
persistent challenge, primarily attributed to the limited aperture size and its
consequent impact on imaging resolution. The existing imaging solution enables
tasks such as pose estimation, activity recognition, and human tracking based
on deep neural networks by estimating skeleton joints. In contrast to
estimating joints, this paper proposes to improve imaging resolution by
estimating the human figure as a whole using conditional generative adversarial
networks (cGAN). In order to reduce training complexity, we use an estimated
spatial spectrum using the MUltiple SIgnal Classification (MUSIC) algorithm as
input to the cGAN. Our system generates environmentally independent,
high-resolution images that can extract unique physical features useful for
human identification. We use a simple convolution layers-based classification
network to obtain the final identification result. From the experimental
results, we show that resolution of the image produced by our trained generator
is high enough to enable human identification. Our finding indicates
high-resolution accuracy with 5% mean silhouette difference to the Kinect
device. Extensive experiments in different environments on multiple testers
demonstrate that our system can achieve 93% overall test accuracy in unseen
environments for static human target identification.
