Human Subject Identification via Passive Spectrum Monitoring

Human subjects' identification including face recognition, fingerprint recognition, and gait recognition enhances biometric health and safety. However, existing methods have their limitations as it is difficult to identify humans when humans cannot touch devices or there is a dim environment. This paper proposes a novel human identification approach, which utilizes passive radio frequency (RF) signal as a biometrics modality to achieve human identification. The passive human subject identification with radio-frequency (PHSIR) approach is verifies that different human subjects could generate different spectrum signatures, and these spectrum characteristics can be distinguished by machine learning (ML) algorithms to achieve human subjects' classification. Software-defined radio (SDR) technology acquires passive RF in the frequency bands that are sensitive to human occupancy. The passive spectrums were collected in two environments. Four ML algorithms were used to classify the sample spectrums associated with different human subjects, including decision tree, support vector machines (SVM), k-nearest neighbors (KNN), and random forest. Experimental results from seven volunteers indicate the classification accuracy is higher than 94% for seven volunteers using KNN algorithms.